WO2017012491A1 - 一种图像高维特征的相似比较方法及装置 - Google Patents
一种图像高维特征的相似比较方法及装置 Download PDFInfo
- Publication number
- WO2017012491A1 WO2017012491A1 PCT/CN2016/089866 CN2016089866W WO2017012491A1 WO 2017012491 A1 WO2017012491 A1 WO 2017012491A1 CN 2016089866 W CN2016089866 W CN 2016089866W WO 2017012491 A1 WO2017012491 A1 WO 2017012491A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- low
- dimensional feature
- feature vector
- image
- segmentation
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 239000013598 vector Substances 0.000 claims abstract description 122
- 230000011218 segmentation Effects 0.000 claims abstract description 56
- 230000009467 reduction Effects 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 6
- 238000005259 measurement Methods 0.000 claims abstract description 4
- 238000013528 artificial neural network Methods 0.000 claims description 13
- 238000013527 convolutional neural network Methods 0.000 claims description 13
- 238000012935 Averaging Methods 0.000 claims description 12
- 238000013135 deep learning Methods 0.000 claims description 7
- 238000010200 validation analysis Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 description 16
- 238000010586 diagram Methods 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 238000011946 reduction process Methods 0.000 description 2
- 238000011524 similarity measure Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2255—Hash tables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
- G06F16/316—Indexing structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
- G06F16/532—Query formulation, e.g. graphical querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/7715—Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Definitions
- the local sensitive hash encoding process of the image is shown in Figure 1.
- the original image is represented as an n-dimensional vector for the RGB channel.
- CNN convolutional neural network
- the output of a hidden layer (usually taken between the reciprocal 2 and 4 layers) is taken as the m-dimensional feature vector of the image (typical The length is 4096 dimensions).
- the eigenvector is then converted to an LSH code of k (k is much smaller than m) by a set of locally sensitive hash vector operations.
- the service company converts all of its pictures into such a code, and subsequent image similarity retrieval is based on the representation of the image.
- the image similarity retrieval process is shown in Figure 2.
- the user-acquired query image first obtains its local-sensitive hash LSH code A through the above step 1, and then calculates the Hamming distance of both A and B for each vector B in the locally-sensitive hash vector table, and then according to the obtained The distance is sorted from small to large, and the original image corresponding to several vectors before the last call is returned to the user as a result of the similar search.
- a similarity comparison method for image high-dimensional features of the present invention includes: performing a dimensionality reduction process on the extracted image feature vector to obtain a low-dimensional feature vector; and equally segmenting the low-dimensional feature vector and Establishing a segmentation index table; obtaining a candidate sample set by retrieving the segmented low-dimensional feature vector of the query image in the segmentation index table; and respectively lowering the samples in the candidate sample set with the query image
- the dimensional feature vector is similarly metric.
- the image feature vector is extracted by constructing a neural network using a deep learning technique.
- the neural network is a convolutional neural network.
- the step of obtaining the candidate sample set by retrieving the segmented low-dimensional feature vector of the query image in the segment index table comprises: accessing the segmented low-dimensional feature vector of the query image by accessing The entry address, the conflict set is obtained; the low-dimensional feature vector corresponding to the conflict set node with the same fingerprint value of the segmented low-dimensional feature vector of the query image is extracted as a candidate set; Segmented search The candidate sets are combined, and the low-dimensional feature vector repeated therein is removed to obtain a candidate sample set.
- performing a similarity measure between the samples in the candidate sample set and the low-dimensional feature vector of the query image respectively includes: calculating, respectively, each sample in the candidate sample set and a low-dimensional feature vector of the query image a Manhattan distance score; the scores are sorted from small to large, and an image of a sample corresponding to a predetermined number of scores is taken as a similar image of the query image.
- the segmentation index is added to the locally sensitive hash code of the image feature vector after dimension reduction, which can speed up the retrieval speed and improve the retrieval efficiency.
- the Manhattan distance metric is used to replace the traditional Hamming. The distance metric can make full use of the quantizable distance information contained in the local sensitive hash coding, thereby improving the retrieval precision under the segmentation retrieval.
- FIG. 1 is a schematic diagram of a locally sensitive hash encoding process for generating an image in the prior art
- FIG. 3 is a schematic diagram showing main steps of a similarity comparison method for image high-dimensional features according to an embodiment of the present invention
- FIG. 5 is a structural diagram of a segmentation hash index table according to an embodiment of the present invention.
- FIG. 6 is a schematic diagram of an image similarity retrieval process based on a segmentation index according to an embodiment of the present invention
- FIG. 3 is a schematic diagram showing the main steps of a similarity comparison method for image high-dimensional features according to an embodiment of the invention.
- a similarity comparison method of an image high-dimensional feature of the present invention includes the following steps S31 to S34.
- Step S31 The extracted image feature vector is subjected to dimensionality reduction processing by a local sensitive hash algorithm to obtain a low-dimensional feature vector.
- the feature vector of the image in this step can be extracted by using a deep learning technique to construct a neural network, and the neural network generally used is a convolutional neural network CNN.
- the neural network generally used is a convolutional neural network CNN.
- the original image is extracted by the convolutional neural network CNN to obtain an m-dimension.
- CNN feature vector is subjected to dimensionality reduction processing by a locally sensitive hash LSH, and a feature vector LSH code with a low (eg, k, k is much smaller than m) dimension is obtained.
- Step S32 averaging the low-dimensional feature vectors and establishing a segmentation index table.
- the optimal segment length can be determined experimentally on a smaller verification set, so that the technical solution of the present invention obtains a search speed and accuracy. Relatively satisfactory compromise. After that, according to the most determined The segment length d is excellent, and the low-dimensional LSH code vector is averaged.
- the LSH code for a sample numbered 1001 is:
- the number before each paragraph represents the positioning number, the first paragraph is 1, the second paragraph is 2, and so on.
- Step S321 averaging the low-dimensional feature vector, taking the segmented feature vector as an index item, and calculating a fingerprint value of each index item;
- Step S322 Perform a remainder operation on the prime value that is closest to the predetermined number of entries included in the segment index table to obtain an entry address of the index entry.
- Step S323 Insert the low-dimensional feature vector into the segment index table according to the obtained entry address to establish a segment index table.
- the sample numbered 1001 is taken as an example.
- the segmented feature vector is used as an index term to calculate the fingerprint value of each segment index entry, for example, the fifth version of the message digest algorithm (ie, the MD5 function) is used for calculation.
- the fingerprint values are compared with the prime numbers closest to the predetermined number of entries included in the segment index table to obtain the entry addresses of the respective index entries.
- the number of entries N of the segment index table can be set as needed, for example, according to the memory size of the machine or the need for function implementation.
- the entry addresses of the index entries may be repeated, that is, the so-called key value conflicts, and the index entry of the key value conflict is in the form of a conflict set. Save as a conflicting set linked list.
- the low-dimensional feature vector LSH encoding is inserted into the segment index table according to the obtained entry address to establish a segment index table.
- Step S33 The candidate sample set is obtained by searching the segmented low-dimensional feature vector of the query image in the segmentation index table.
- Step S331 Obtain a conflict set by accessing an entry address of the segmented low-dimensional feature vector of the query image.
- Step S332 Extracting the low-dimensional feature vector corresponding to the conflict set node of the segmented low-dimensional feature vector of the query image as a candidate set;
- Step S333 Combine the candidate sets obtained by each segment retrieval, and remove the low-dimensional feature vector repeated therein to obtain a candidate sample set.
- Step S331 first, calculating a fingerprint value of each segmented index item after segmentation to find an entry address S of each index entry in the index table, and obtaining a corresponding conflict set linked list by accessing the entry address S, The pointer to the right of each node in the conflict set list is used to save the conflict set list, and the downward pointer is used to save the candidate set list.
- the so-called candidate set is that when the fingerprint value of a segment index item of a certain query image is the same as the fingerprint value of the "segmented local sensitive hash LSH code" of the node, the segment index that needs to be carefully compared later is compared.
- the candidate sample set of the item where the candidate sample is the corresponding low-dimensional feature vector.
- the candidate set is extracted as described in step S332.
- step S333 after each segment retrieval is completed, all the obtained candidate sets are combined, and the repeated low-dimensional feature vectors are removed to obtain a candidate sample set that needs to be compared by distance.
- Step S34 Perform similar metrics on the samples in the candidate sample set and the low-dimensional feature vectors of the query image.
- Similarity measurement first, respectively calculating a Manhattan distance score between each sample in the candidate sample set and a low-dimensional feature vector of the query image; then, sorting the scores from small to large, and taking The image of the sample corresponding to the score of the previous predetermined number is taken as the similar image of the query image.
- the number of similar images to be displayed may be set in advance according to the needs of the application, or the number of similar images to be displayed may be selected by the user.
- the process of segment-based index-based image similarity retrieval according to an embodiment of the present invention as shown in FIG. 6 can be realized. That is, the low-dimensional feature vector of the query image and each element in the segmented local-sensitive hash LSH coding vector set are respectively based on the similarity between the segmentation index and the Manhattan distance, and finally the similar local-sensitive hash LSH coded record set is obtained. , thereby obtaining a corresponding similar image.
- the local sensitive hash code contains quantizable distance information
- the distance information is fully utilized when the Manhattan distance is used for similarity measurement, and the Hamming distance is only equal to the number comparison, so the similarity measure based on the Manhattan distance is used.
- the retrieval accuracy is higher than the Hamming distance based scheme. After a large amount of experimental data, the retrieval accuracy is about 5% higher.
- FIG. 7 is a schematic diagram of main modules of a similarity comparing apparatus for image high-dimensional features according to an embodiment of the invention. As shown in FIG. 7, the phase of the image high-dimensional feature in the embodiment of the present invention
- the comparison device 70 mainly includes a feature dimension reduction module 71, a segmentation index module 72, a similarity query module 73, and a comparison metric module 74.
- the feature dimension reduction module 71 is configured to perform the dimension reduction processing on the extracted image feature vector by a local sensitive hash algorithm to obtain a low-dimensional feature vector.
- the image feature vector is extracted by constructing a neural network by using a deep learning technique, and the neural network is a convolutional neural network.
- the segmentation indexing module 72 is configured to average segment the low-dimensional feature vectors and establish a segmentation index table. Segmentation indexing module 72 may also be used to experimentally determine an optimal segment length on a smaller set of validations prior to averaging the low-dimensional feature vectors.
- the segmentation indexing module 72 may be further configured to average segment the low-dimensional feature vector, use the segmented feature vector as an index item, and calculate a fingerprint value of each index item;
- the segmentation index table includes a predetermined number of entries, and the nearest prime number is subjected to a remainder operation to obtain an entry address of the index entry; the low-dimensional feature vector is inserted into the segment index table according to the obtained entry address to establish segmentation. direction chart.
- the similarity query module 73 is configured to obtain a candidate sample set by searching the segmented low-dimensional feature vector of the query image in the segmentation index table.
- the similarity query module 73 can also be configured to obtain a conflict set by accessing the entry address of the segmented low-dimensional feature vector of the query image; and set a conflict set with the same fingerprint value of the segmented low-dimensional feature vector of the query image.
- the low-dimensional feature vector corresponding to the node is extracted as a candidate set; and the candidate sets retrieved by each segment are combined, and the repeated low-dimensional feature vectors are removed to obtain a candidate sample set.
- the comparison metric module 74 is configured to perform similar metrics on the samples in the candidate sample set with the low-dimensional feature vectors of the query image.
- the comparison metric module 74 can be further configured to separately calculate a Manhattan distance score between each sample in the candidate sample set and a low-dimensional feature vector of the query image; sort the scores from small to large, and pre-schedule The image of the sample corresponding to the score of the number is taken as the similar image of the query image.
- the segmentation index is added to the locally sensitive hash code of the reduced image feature vector to speed up the retrieval speed, thereby improving the retrieval efficiency; when performing image similarity retrieval, the Manhattan distance metric is used instead of the traditional one.
- the Hamming distance metric can make full use of the quantizable distance information contained in the local sensitive hash coding, thereby improving the retrieval precision under the segmentation retrieval.
- the process of establishing a segmentation index in the technical solution of the present invention may not be limited to being executed on one machine, and may be performed in parallel in a distributed scheduling system. This way, you can handle larger amounts of data.
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Algebra (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Library & Information Science (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Molecular Biology (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Image Analysis (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
Description
Claims (14)
- 一种图像高维特征的相似比较方法,其特征在于,包括:将所提取的图像特征向量通过局部敏感哈希算法进行降维处理后得到低维特征向量;将所述低维特征向量平均分段并建立分段索引表;通过将查询图像的分段后的低维特征向量在所述分段索引表中进行检索得到候选样本集;将所述候选样本集中的样本分别与所述查询图像的低维特征向量进行相似度量。
- 根据权利要求1所述的方法,其特征在于,所述图像特征向量是利用深度学习技术构建神经网络来提取的。
- 根据权利要求2所述的方法,其特征在于,所述神经网络为卷积神经网络。
- 根据权利要求1所述的方法,其特征在于,将所述低维特征向量平均分段的步骤之前,还包括:在一个较小的验证集上通过实验确定最优的分段长度。
- 根据权利要求1所述的方法,其特征在于,将所述低维特征向量平均分段并建立分段索引表的步骤包括:将所述低维特征向量平均分段,以分段后的特征向量作为索引项,并计算每个索引项的指纹值;将所述指纹值对与所述分段索引表包含的预定的入口个数最接近的素数进行取余运算,得到所述索引项的入口地址;将所述低维特征向量根据得到的所述入口地址***所述分段索引表中以建立分段索引表。
- 根据权利要求1所述的方法,其特征在于,通过将查询图像的分段后的低维特征向量在所述分段索引表中进行检索得到候选样本集的步骤包括:通过访问查询图像的所述分段后的低维特征向量的入口地址,得到冲突集;将与所述查询图像的分段后的低维特征向量的指纹值相同的所述冲突集节点对应的所述低维特征向量提取出来,作为候选集;把各个分段检索得到的所述候选集合并起来,去掉其中重复的所述低维特征向量,得到候选样本集。
- 根据权利要求1所述的方法,其特征在于,将所述候选样本集中的样本分别与所述查询图像的低维特征向量进行相似度量包括:分别计算所述候选样本集中的每个样本与所述查询图像的低维特征向量之间的曼哈顿距离得分;将所述得分按照从小到大排序,并取前预定个数的得分对应的样本的图像作为所述查询图像的相似图像。
- 一种图像高维特征的相似比较装置,其特征在于,包括:特征降维模块,用于将所提取的图像特征向量通过局部敏感哈希算法进行降维处理后得到低维特征向量;分段索引模块,用于将所述低维特征向量平均分段并建立分段索引表;相似查询模块,用于通过将查询图像的分段后的低维特征向量在所述分段索引表中进行检索得到候选样本集;比较度量模块,用于将所述候选样本集中的样本分别与所述查询图像的低维特征向量进行相似度量。
- 根据权利要求8所述的装置,其特征在于,所述图像特征向量是利用深度学习技术构建神经网络来提取的。
- 根据权利要求9所述的装置,其特征在于,所述神经网络为卷积神经网络。
- 根据权利要求8所述的装置,其特征在于,所述分段索引模块在将所述低维特征向量平均分段之前,还用于:在一个较小的验证集上通过实验确定最优的分段长度。
- 根据权利要求8所述的装置,其特征在于,所述分段索引模块还用于:将所述低维特征向量平均分段,以分段后的特征向量作为索引项,并计算每个索引项的指纹值;将所述指纹值对与所述分段索引表包含的预定的入口个数最接近的素数进行取余运算,得到所述索引项的入口地址;将所述低维特征向量根据得到的所述入口地址***所述分段索引表中以建立分段索引表。
- 根据权利要求8所述的装置,其特征在于,所述相似查询模块还用于:通过访问查询图像的所述分段后的低维特征向量的入口地址,得到冲突集;将与所述查询图像的分段后的低维特征向量的指纹值相同的所述冲突集节点对应的所述低维特征向量提取出来,作为候选集;把各个分段检索得到的所述候选集合并起来,去掉其中重复的所述低维特征向量,得到候选样本集。
- 根据权利要求8所述的装置,其特征在于,所述比较度量模块还用于:分别计算所述候选样本集中的每个样本与所述查询图像的低维特征向量之间的曼哈顿距离得分;将所述得分按照从小到大排序,并取前预定个数的得分对应的样 本的图像作为所述查询图像的相似图像。
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/746,794 US11048966B2 (en) | 2015-07-23 | 2016-07-13 | Method and device for comparing similarities of high dimensional features of images |
JP2018502395A JP6544756B2 (ja) | 2015-07-23 | 2016-07-13 | 画像の高次元特徴の類似性を比較するための方法及びデバイス |
RU2018106458A RU2686590C1 (ru) | 2015-07-23 | 2016-07-13 | Способ и устройство для сравнения схожих элементов высокоразмерных признаков изображений |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510436176.6A CN105095435A (zh) | 2015-07-23 | 2015-07-23 | 一种图像高维特征的相似比较方法及装置 |
CN201510436176.6 | 2015-07-23 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2017012491A1 true WO2017012491A1 (zh) | 2017-01-26 |
Family
ID=54575872
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2016/089866 WO2017012491A1 (zh) | 2015-07-23 | 2016-07-13 | 一种图像高维特征的相似比较方法及装置 |
Country Status (5)
Country | Link |
---|---|
US (1) | US11048966B2 (zh) |
JP (1) | JP6544756B2 (zh) |
CN (1) | CN105095435A (zh) |
RU (1) | RU2686590C1 (zh) |
WO (1) | WO2017012491A1 (zh) |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107992892A (zh) * | 2017-12-05 | 2018-05-04 | 奕响(大连)科技有限公司 | 一种像素灰度图片相似判定方法 |
CN108038436A (zh) * | 2017-12-05 | 2018-05-15 | 奕响(大连)科技有限公司 | 一种局部像素灰度图片相似判定方法 |
CN109271545A (zh) * | 2018-08-02 | 2019-01-25 | 深圳市商汤科技有限公司 | 一种特征检索方法及装置、存储介质和计算机设备 |
CN110209895A (zh) * | 2019-06-06 | 2019-09-06 | 阿里巴巴集团控股有限公司 | 向量检索方法、装置和设备 |
CN111460088A (zh) * | 2019-01-22 | 2020-07-28 | 阿里巴巴集团控股有限公司 | 相似文本的检索方法、装置和*** |
CN111581413A (zh) * | 2020-04-03 | 2020-08-25 | 北京联合大学 | 一种面向高维图像数据检索的数据过滤方法及*** |
CN112307248A (zh) * | 2020-11-26 | 2021-02-02 | 国网电子商务有限公司 | 一种图像检索方法及装置 |
CN113436188A (zh) * | 2021-07-28 | 2021-09-24 | 北京计算机技术及应用研究所 | 一种利用卷积计算图像哈希值的方法 |
US11210555B2 (en) | 2017-03-17 | 2021-12-28 | Beijing Jingdong Shangke Information Technology Co., Ltd. | High-dimensional image feature matching method and device |
CN114445811A (zh) * | 2022-01-30 | 2022-05-06 | 北京百度网讯科技有限公司 | 一种图像处理方法、装置及电子设备 |
CN114978783A (zh) * | 2022-08-02 | 2022-08-30 | 暗链科技(深圳)有限公司 | 零知识身份验证方法、验证客户端、用户客户端及*** |
CN115357747A (zh) * | 2022-10-18 | 2022-11-18 | 山东建筑大学 | 一种基于序数哈希的图像检索方法及*** |
Families Citing this family (42)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105095435A (zh) | 2015-07-23 | 2015-11-25 | 北京京东尚科信息技术有限公司 | 一种图像高维特征的相似比较方法及装置 |
CN105512273A (zh) * | 2015-12-03 | 2016-04-20 | 中山大学 | 一种基于可变长深度哈希学习的图像检索方法 |
CN105721933A (zh) * | 2016-03-23 | 2016-06-29 | Tcl集团股份有限公司 | 广告视频信息库的创建方法、广告视频识别方法及装置 |
CN105844667A (zh) * | 2016-03-25 | 2016-08-10 | 中国矿业大学 | 一种紧凑颜色编码的结构化目标跟踪方法 |
CN107423309A (zh) * | 2016-06-01 | 2017-12-01 | 国家计算机网络与信息安全管理中心 | 基于模糊哈希算法的海量互联网相似图片检测***及方法 |
CN106227851B (zh) * | 2016-07-29 | 2019-10-01 | 汤一平 | 基于深度卷积神经网络的分层深度搜索的图像检索方法 |
CN108629233A (zh) * | 2017-03-20 | 2018-10-09 | 华为技术有限公司 | 一种行人检索方法及装置 |
CN107220325A (zh) * | 2017-05-22 | 2017-09-29 | 华中科技大学 | 一种基于卷积神经网络的app相似图标检索方法和*** |
CN107273471A (zh) * | 2017-06-07 | 2017-10-20 | 国网上海市电力公司 | 一种基于Geohash的二元电力时序数据索引构建方法 |
CN110019905B (zh) * | 2017-10-13 | 2022-02-01 | 北京京东尚科信息技术有限公司 | 信息输出方法和装置 |
CN108876864B (zh) * | 2017-11-03 | 2022-03-08 | 北京旷视科技有限公司 | 图像编码、解码方法、装置、电子设备及计算机可读介质 |
CN108021693A (zh) * | 2017-12-18 | 2018-05-11 | 北京奇艺世纪科技有限公司 | 一种图像检索方法和装置 |
CN109416689B (zh) * | 2018-01-16 | 2021-08-03 | 深圳力维智联技术有限公司 | 海量特征向量数据的相似检索方法及设备、存储介质 |
CN108959441A (zh) * | 2018-06-13 | 2018-12-07 | 新华智云科技有限公司 | 一种基于局部敏感哈希的近相似快速查找方法 |
CN109165307B (zh) * | 2018-09-19 | 2021-02-02 | 腾讯科技(深圳)有限公司 | 一种特征检索方法、装置和存储介质 |
CN111126102A (zh) * | 2018-10-30 | 2020-05-08 | 富士通株式会社 | 人员搜索方法、装置及图像处理设备 |
CN110149529B (zh) * | 2018-11-01 | 2021-05-28 | 腾讯科技(深圳)有限公司 | 媒体信息的处理方法、服务器及存储介质 |
CN109543057A (zh) * | 2018-11-20 | 2019-03-29 | 广州逗号智能零售有限公司 | 基于智能收银台的商品识别方法、装置、设备及存储介质 |
CN109582674B (zh) * | 2018-11-28 | 2023-12-22 | 亚信科技(南京)有限公司 | 一种数据存储方法及*** |
CN109753576A (zh) * | 2018-12-25 | 2019-05-14 | 上海七印信息科技有限公司 | 一种相似图像检索方法 |
CN111506756B (zh) * | 2019-01-30 | 2024-05-17 | 北京京东尚科信息技术有限公司 | 相似图片的查找方法及***、电子设备、存储介质 |
US10810726B2 (en) | 2019-01-30 | 2020-10-20 | Walmart Apollo, Llc | Systems and methods for detecting content in images using neural network architectures |
US10922584B2 (en) | 2019-01-30 | 2021-02-16 | Walmart Apollo, Llc | Systems, methods, and techniques for training neural networks and utilizing the neural networks to detect non-compliant content |
CN110059634B (zh) * | 2019-04-19 | 2023-04-18 | 山东博昂信息科技有限公司 | 一种大场景人脸抓拍方法 |
KR20210033774A (ko) | 2019-09-19 | 2021-03-29 | 삼성전자주식회사 | 이미지 분석 방법 및 이를 수행하는 전자 장치 |
CN111143597B (zh) * | 2019-12-13 | 2023-06-20 | 浙江大华技术股份有限公司 | 图像检索方法、终端及存储装置 |
CN111008301B (zh) * | 2019-12-19 | 2023-08-15 | 新华智云科技有限公司 | 一种以图搜视频的方法 |
CN111191058B (zh) * | 2019-12-27 | 2023-08-29 | 青岛海洋科技中心 | 一种图片检索方法 |
US11758069B2 (en) | 2020-01-27 | 2023-09-12 | Walmart Apollo, Llc | Systems and methods for identifying non-compliant images using neural network architectures |
CN111325245B (zh) * | 2020-02-05 | 2023-10-17 | 腾讯科技(深圳)有限公司 | 重复图像识别方法、装置、电子设备及计算机可读存储介质 |
CN111324760B (zh) * | 2020-02-19 | 2023-09-26 | 创优数字科技(广东)有限公司 | 一种图像检索方法及装置 |
CN111738194B (zh) * | 2020-06-29 | 2024-02-02 | 深圳力维智联技术有限公司 | 一种用于人脸图像相似性的评价方法和装置 |
CN111737586B (zh) * | 2020-08-19 | 2020-12-04 | 腾讯科技(深圳)有限公司 | 信息推荐方法、装置、设备及计算机可读存储介质 |
CN112100412B (zh) * | 2020-09-01 | 2024-04-12 | 深圳市欢太科技有限公司 | 图片检索方法、装置、计算机设备和存储介质 |
CN112000940B (zh) * | 2020-09-11 | 2022-07-12 | 支付宝(杭州)信息技术有限公司 | 一种隐私保护下的用户识别方法、装置以及设备 |
CN112417381B (zh) * | 2020-12-11 | 2021-06-22 | 中国搜索信息科技股份有限公司 | 应用于图像版权保护的快速定位侵权图像的方法及装置 |
JP7200279B2 (ja) * | 2021-03-03 | 2023-01-06 | 三菱電機インフォメーションシステムズ株式会社 | 検知装置、検知方法、検知プログラム及び検知システム |
CN113127515A (zh) * | 2021-04-12 | 2021-07-16 | 中国电力科学研究院有限公司 | 面向电网的调控数据高速缓存方法、装置、计算机设备和存储介质 |
CN114595350B (zh) * | 2021-12-08 | 2024-04-26 | 拓尔思信息技术股份有限公司 | 一种百亿级图像快速搜索的方法 |
CN114795468B (zh) * | 2022-04-19 | 2022-11-15 | 首都医科大学附属北京天坛医院 | 一种血管内治疗的术中导航方法及其*** |
CN114943090B (zh) * | 2022-07-22 | 2022-11-22 | 图灵人工智能研究院(南京)有限公司 | 一种基于同态加密大规模人脸库隐匿查询的方法 |
CN117390013A (zh) * | 2023-09-12 | 2024-01-12 | 博瀚智能(深圳)有限公司 | 数据存储方法、检索方法、***、设备及存储介质 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103345496A (zh) * | 2013-06-28 | 2013-10-09 | 新浪网技术(中国)有限公司 | 多媒体信息检索方法和*** |
CN104050247A (zh) * | 2014-06-04 | 2014-09-17 | 上海美琦浦悦通讯科技有限公司 | 实现海量视频快速检索的方法 |
CN104572804A (zh) * | 2013-10-24 | 2015-04-29 | Tcl集团股份有限公司 | 一种视频物体检索的方法及其*** |
CN105095435A (zh) * | 2015-07-23 | 2015-11-25 | 北京京东尚科信息技术有限公司 | 一种图像高维特征的相似比较方法及装置 |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7353224B2 (en) * | 2001-12-04 | 2008-04-01 | Hewlett-Packard Development Company, L.P. | System and method for efficiently finding near-similar images in massive databases |
US20070276733A1 (en) * | 2004-06-23 | 2007-11-29 | Frank Geshwind | Method and system for music information retrieval |
US7657126B2 (en) * | 2005-05-09 | 2010-02-02 | Like.Com | System and method for search portions of objects in images and features thereof |
CN101710334B (zh) * | 2009-12-04 | 2012-01-25 | 大连理工大学 | 基于图像哈希的大规模图像库检索方法 |
US20110142335A1 (en) * | 2009-12-11 | 2011-06-16 | Bernard Ghanem | Image Comparison System and Method |
US8488883B2 (en) * | 2009-12-28 | 2013-07-16 | Picscout (Israel) Ltd. | Robust and efficient image identification |
US8515964B2 (en) * | 2011-07-25 | 2013-08-20 | Yahoo! Inc. | Method and system for fast similarity computation in high dimensional space |
RU2480831C1 (ru) * | 2011-11-24 | 2013-04-27 | Общество с ограниченной ответственностью "КБК Групп" | Способ выборки изображений из базы изображений |
US9208219B2 (en) | 2012-02-09 | 2015-12-08 | Stroz Friedberg, LLC | Similar document detection and electronic discovery |
US20150169644A1 (en) * | 2013-01-03 | 2015-06-18 | Google Inc. | Shape-Gain Sketches for Fast Image Similarity Search |
CN103440292B (zh) * | 2013-08-16 | 2016-12-28 | 新浪网技术(中国)有限公司 | 基于比特向量的多媒体信息检索方法和*** |
US9412031B2 (en) | 2013-10-16 | 2016-08-09 | Xerox Corporation | Delayed vehicle identification for privacy enforcement |
JP6211407B2 (ja) * | 2013-12-06 | 2017-10-11 | 株式会社デンソーアイティーラボラトリ | 画像検索システム、画像検索装置、検索サーバ装置、画像検索方法、及び画像検索プログラム |
CN104035949B (zh) * | 2013-12-10 | 2017-05-10 | 南京信息工程大学 | 一种基于局部敏感哈希改进算法的相似性数据检索方法 |
US11100073B2 (en) * | 2015-11-12 | 2021-08-24 | Verizon Media Inc. | Method and system for data assignment in a distributed system |
-
2015
- 2015-07-23 CN CN201510436176.6A patent/CN105095435A/zh active Pending
-
2016
- 2016-07-13 US US15/746,794 patent/US11048966B2/en active Active
- 2016-07-13 RU RU2018106458A patent/RU2686590C1/ru active
- 2016-07-13 JP JP2018502395A patent/JP6544756B2/ja active Active
- 2016-07-13 WO PCT/CN2016/089866 patent/WO2017012491A1/zh active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103345496A (zh) * | 2013-06-28 | 2013-10-09 | 新浪网技术(中国)有限公司 | 多媒体信息检索方法和*** |
CN104572804A (zh) * | 2013-10-24 | 2015-04-29 | Tcl集团股份有限公司 | 一种视频物体检索的方法及其*** |
CN104050247A (zh) * | 2014-06-04 | 2014-09-17 | 上海美琦浦悦通讯科技有限公司 | 实现海量视频快速检索的方法 |
CN105095435A (zh) * | 2015-07-23 | 2015-11-25 | 北京京东尚科信息技术有限公司 | 一种图像高维特征的相似比较方法及装置 |
Cited By (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11210555B2 (en) | 2017-03-17 | 2021-12-28 | Beijing Jingdong Shangke Information Technology Co., Ltd. | High-dimensional image feature matching method and device |
CN107992892A (zh) * | 2017-12-05 | 2018-05-04 | 奕响(大连)科技有限公司 | 一种像素灰度图片相似判定方法 |
CN108038436A (zh) * | 2017-12-05 | 2018-05-15 | 奕响(大连)科技有限公司 | 一种局部像素灰度图片相似判定方法 |
CN109271545A (zh) * | 2018-08-02 | 2019-01-25 | 深圳市商汤科技有限公司 | 一种特征检索方法及装置、存储介质和计算机设备 |
CN111460088A (zh) * | 2019-01-22 | 2020-07-28 | 阿里巴巴集团控股有限公司 | 相似文本的检索方法、装置和*** |
CN110209895A (zh) * | 2019-06-06 | 2019-09-06 | 阿里巴巴集团控股有限公司 | 向量检索方法、装置和设备 |
CN110209895B (zh) * | 2019-06-06 | 2023-09-05 | 创新先进技术有限公司 | 向量检索方法、装置和设备 |
CN111581413B (zh) * | 2020-04-03 | 2023-02-28 | 北京联合大学 | 一种面向高维图像数据检索的数据过滤方法及*** |
CN111581413A (zh) * | 2020-04-03 | 2020-08-25 | 北京联合大学 | 一种面向高维图像数据检索的数据过滤方法及*** |
CN112307248A (zh) * | 2020-11-26 | 2021-02-02 | 国网电子商务有限公司 | 一种图像检索方法及装置 |
CN112307248B (zh) * | 2020-11-26 | 2023-11-03 | 国网数字科技控股有限公司 | 一种图像检索方法及装置 |
CN113436188A (zh) * | 2021-07-28 | 2021-09-24 | 北京计算机技术及应用研究所 | 一种利用卷积计算图像哈希值的方法 |
CN113436188B (zh) * | 2021-07-28 | 2023-02-03 | 北京计算机技术及应用研究所 | 一种利用卷积计算图像哈希值的方法 |
CN114445811A (zh) * | 2022-01-30 | 2022-05-06 | 北京百度网讯科技有限公司 | 一种图像处理方法、装置及电子设备 |
CN114978783A (zh) * | 2022-08-02 | 2022-08-30 | 暗链科技(深圳)有限公司 | 零知识身份验证方法、验证客户端、用户客户端及*** |
CN114978783B (zh) * | 2022-08-02 | 2022-11-11 | 暗链科技(深圳)有限公司 | 零知识身份验证方法、验证客户端、用户客户端及*** |
CN115357747A (zh) * | 2022-10-18 | 2022-11-18 | 山东建筑大学 | 一种基于序数哈希的图像检索方法及*** |
CN115357747B (zh) * | 2022-10-18 | 2024-03-26 | 山东建筑大学 | 一种基于序数哈希的图像检索方法及*** |
Also Published As
Publication number | Publication date |
---|---|
JP2018527656A (ja) | 2018-09-20 |
US11048966B2 (en) | 2021-06-29 |
CN105095435A (zh) | 2015-11-25 |
JP6544756B2 (ja) | 2019-07-17 |
US20180349735A1 (en) | 2018-12-06 |
RU2686590C1 (ru) | 2019-04-29 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2017012491A1 (zh) | 一种图像高维特征的相似比较方法及装置 | |
CN106777318B (zh) | 基于协同训练的矩阵分解跨模态哈希检索方法 | |
Liu et al. | Collaborative hashing | |
WO2020207074A1 (zh) | 一种信息推送的方法及设备 | |
CN106570141B (zh) | 近似重复图像检测方法 | |
US11727053B2 (en) | Entity recognition from an image | |
US8868569B2 (en) | Methods for detecting and removing duplicates in video search results | |
CN109710792B (zh) | 一种基于索引的快速人脸检索***应用 | |
WO2023108980A1 (zh) | 基于文本对抗样例的信息推送方法及装置 | |
Xie et al. | Fast and accurate near-duplicate image search with affinity propagation on the ImageWeb | |
CN110334290B (zh) | 一种基于MF-Octree的时空数据快速检索方法 | |
CN110134965B (zh) | 用于信息处理的方法、装置、设备和计算机可读存储介质 | |
CN110069713B (zh) | 一种基于用户上下文感知的个性化推荐方法 | |
Liu et al. | An image-based near-duplicate video retrieval and localization using improved edit distance | |
CN113239159B (zh) | 基于关系推理网络的视频和文本的跨模态检索方法 | |
CN111339784A (zh) | 一种新话题的自动挖掘方法和*** | |
CN112836008B (zh) | 基于去中心化存储数据的索引建立方法 | |
CN113918807A (zh) | 数据推荐方法、装置、计算设备及计算机可读存储介质 | |
CN106599305B (zh) | 一种基于众包的异构媒体语义融合方法 | |
US10824811B2 (en) | Machine learning data extraction algorithms | |
CN116340551A (zh) | 一种相似内容确定方法和装置 | |
CN115098728A (zh) | 视频检索方法及装置 | |
JP2011159100A (ja) | 逐次類似文書検索装置、逐次類似文書検索方法およびプログラム | |
Ozan et al. | M-pca binary embedding for approximate nearest neighbor search | |
CN111191058A (zh) | 一种图片检索方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 16827182 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2018502395 Country of ref document: JP Kind code of ref document: A |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2018106458 Country of ref document: RU |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC , EPO FORM 1205A DATED 05.04.18. |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 16827182 Country of ref document: EP Kind code of ref document: A1 |