CN108256519B - Infrared image significance detection method based on global and local interaction - Google Patents

Infrared image significance detection method based on global and local interaction Download PDF

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CN108256519B
CN108256519B CN201711322406.1A CN201711322406A CN108256519B CN 108256519 B CN108256519 B CN 108256519B CN 201711322406 A CN201711322406 A CN 201711322406A CN 108256519 B CN108256519 B CN 108256519B
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徐晓川
祁伟
曹峰
杨粤涛
刘光胜
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Suzhou Changfeng Aviation Electronics Co Ltd
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Abstract

The invention discloses an infrared image saliency detection method based on global and local interaction, which comprises the steps of calculating a structural local self-adaptive recursive kernel, constructing an affine matrix to improve saliency map performance, establishing global constraint based on a Gaussian mixture model, filtering noise interference by using a structural filtering method, and integrating local and global models to calculate a final saliency map. The invention can effectively estimate the visual salient target information, improve the effective follow-up area for the target tracking and target identification in the later period, reduce the search consumption of the machine vision algorithm, improve the operation efficiency of the algorithm, reduce the operation power consumption of hardware, improve the resource utilization rate of image signals and provide effective image preprocessing support for the visual task in the later period.

Description

Infrared image significance detection method based on global and local interaction
Technical Field
The invention relates to an infrared image significance detection method, in particular to an infrared image significance detection method based on global and local interaction, and belongs to the technical field of image understanding processing.
Background
Saliency detection plays an important role in understanding analysis methods for night vision images (including low-light, infrared images), and it also plays an important role in machine vision applications.
One of documents (x.hou, l.zhang, Dynamic visual attribute: search for coding length entries, in: d.koller, d.schuurmans, y.bengio, l.bottou (Eds.), advance in Neural Information Processing Systems 21,2009.) proposes a Dynamic visual model based on the rarity of features, and applies (ICL) to estimate the gain in entropy of each feature. Document two (t.liu, z.yuan, j.sun, j.wang, n.zheng, x.tang, h. -y.shum, Learning to detect a present object, IEEE TPAMI 33(2),2011.) proposes a method of binary significance detection by training a conditional random field that incorporates a novel set of features such as multi-level contrast, center-around histogram, and color space distribution. However, the above method is proposed based on natural images, and the effect of application to infrared images is not good. Document three (c.n.xinwang, l.xu, scientific detection using spatial consistent-spatial aspects combination, innovative Physics & Technology 72,2015.) uses the luminance contrast and contour features of Infrared images to estimate the Saliency of Infrared images. However, this approach may lead to erroneous estimation results, with the salient regions containing background noise.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides an infrared image saliency detection method based on global and local interaction.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an infrared image significance detection method based on global and local interaction comprises the following steps:
the method comprises the following steps: a structural locally adaptive recursive kernel is computed,
let F denote the feature image extracted from the input image I, and equation (1) is:
F(x,y)＝Γ(I,x,y) (1)
where Γ () represents a multi-dimensional function that extracts image features,
using the position, gradient, brightness, LBP and HOG information of the infrared image as the features of the image, in the feature image F, each pixel can be described as a 7-dimensional vector, and formula (2) is:
Figure GDA0003333787230000021
wherein (x, y) represents the position of the pixel,
Figure GDA0003333787230000022
representing edge information of the pixel, HOG () representing HOG features, Lu () representing luminance features, LBP () representing LBP features,
a certain region R of F in the feature image can be described as a multi-dimensional covariance matrix CRThe formula (3) is:
Figure GDA0003333787230000023
wherein z isi(i ═ 1., k) denotes all the feature points in the region R, and μ denotes ziIs determined by the average value of (a) of (b),
a calculation method of a structural local self-adaptive recursive kernel is disclosed as the following formula (4):
Figure GDA0003333787230000024
wherein, l belongs to [12]，P2Denotes the total number of pixels in the local window, Δ x denotes the coordinate relationship between the center of the window and the surrounding pixels, and s ═ x1，x2，z(x1，x2)}，z(x1，x2) Is a pixel (x)1，x2) The gray value of (a);
step two: an affine matrix is constructed, the performance of the saliency map is improved,
the similarity between two regions is represented by the distance between the structural local adaptive recursive kernels of the two regions, and the relevance w of the structural local adaptive recursive kernel in a region m and the structural local adaptive recursive kernel in another region nmnEquation (5) is:
Figure GDA0003333787230000025
wherein, sl ism，slnMeans representing the structural locally adaptive recursive kernels of the regions m, n, respectively, Q (n) representing a set of neighborhoods of the region n, σ1Is a parameter that controls the degree of similarity, MCS () represents a cosine similarity matrix,
then, a row-normalized affine matrix is constructed, with equation (6) as:
A＝D-1·W (6)
wherein the affine matrix W ═ Wmn]N×NUsed to represent the similarity between any pair of nodes, the angle matrix D ═ diag { D }1，d2，...，dNIn which d isn＝∑nwmnRepresents the sum of the relevance of the region n to all other regions,
based on a given affine matrix, the saliency of a local region is defined by a descriptor of a structural locally adaptive recursive kernel, and formula (7) is:
Figure GDA0003333787230000031
wherein, amnRepresents the degree of association, S, calculated in equation (6)SLARKRepresenting a low-quality saliency map based on structural locally adaptive recursive kernels, N representing the number of structural locally adaptive recursive kernels;
step three: based on the Gaussian mixture model, global constraints are established,
first, a global condition is defined and its cost is minimized, and equation (8) is:
Figure GDA0003333787230000032
wherein, b1，b2A gaussian mixture model representing the foreground and background in the infrared image respectively,
the descriptor of each structural locally adaptive recursive kernel can be regarded as a weight combined with a gaussian mixture model, and belongs to the probability of a neighborhood, and formula (9) is:
Figure GDA0003333787230000033
wherein, PnRepresents a linear operation for extracting the nth structural local recursive kernel region from the infrared image, wmnIs calculated by the formula (5) to obtainmIs a covariance matrix, phi denotes a gaussian distribution;
step four: by using a structural filtering method, noise interference is filtered,
based on a structural local adaptive recursive kernel, a filtering method is designed to further filter background noise contained in a gaussian model, and formula (10) is as follows:
Figure GDA0003333787230000034
wherein S isG(x2) Denotes SGMiddle pixel x2The value of the significance of (a) is,
Figure GDA0003333787230000035
is a normalization factor, R (x)2) Is represented by x1As pixels in the neighborhood of the center of the circle,
Figure GDA0003333787230000036
step five: integrating the local and global models, computing the final saliency map,
the formula (11) is:
S*＝αS1+(1-α)S2 (11)
wherein α is a balance factor, S1Representing a local saliency map, S2Representing a global saliency map.
The invention has the following beneficial effects:
the target structure information can be effectively marked by adopting the area covariance, and the difference between the background and the target can be effectively distinguished; and the global and local information is considered, the salient target information can be effectively mined, the visual saliency can be recalculated through an effective integration frame, and the accuracy of saliency detection is improved. And when the global saliency map is calculated, a Gaussian mixture model is utilized to establish global clue constraint, and the interference of noise is reduced through structural filtering. The method can effectively estimate the visual salient target information, improve effective follow-up areas for target tracking and target identification in the later period, reduce the search consumption of a machine vision algorithm, improve the operation efficiency of the algorithm, reduce the operation power consumption of hardware, improve the resource utilization rate of image signals and provide effective image preprocessing support for visual tasks in the later period.
Drawings
FIG. 1 is a schematic diagram of the infrared image saliency detection method of the present invention.
Fig. 2 is a partial saliency map obtained in step two of the present invention.
FIG. 3 shows the second step of the present invention
Figure GDA0003333787230000042
Local saliency maps at different values are taken.
FIG. 4 is a saliency map of the global model of the present invention.
FIG. 5 shows the fourth step of the present invention
Figure GDA0003333787230000041
Local saliency maps at different values are taken.
FIG. 6 is a graph of the significance detection results of the present invention.
Detailed Description
The invention provides an infrared image significance detection method based on global and local interaction. The technical solution of the present invention is described in detail below with reference to the accompanying drawings so that it can be more easily understood and appreciated.
An infrared image saliency detection method based on global and local interaction, as shown in fig. 1, includes the following steps:
the method comprises the following steps: a structural locally adaptive recursive kernel is computed,
let F denote the feature image extracted from the input image I, and equation (1) is:
F(x，y)＝Γ(I，x，y) (1)
where Γ () represents a multi-dimensional function that extracts image features,
using the position, gradient, brightness, LBP and HOG information of the infrared image as the features of the image, in the feature image F, each pixel can be described as a 7-dimensional vector, and formula (2) is:
Figure GDA0003333787230000051
wherein (x, y) is as followsShowing the location of the pixel(s),
Figure GDA0003333787230000052
representing edge information of the pixel, HOG () representing HOG features, Lu () representing luminance features, LBP () representing LBP features,
a certain region R of F in the feature image can be described as a multi-dimensional covariance matrix CRThe formula (3) is:
Figure GDA0003333787230000053
wherein z isi(i ═ 1., k) denotes all the feature points in the region R, and μ denotes ziIs determined by the average value of (a) of (b),
the calculation method of the structural local self-adaptive recursive kernel has the following formula (4):
Figure GDA0003333787230000054
wherein l belongs to [12]，P2Denotes the total number of pixels in the local window, Δ x denotes the coordinate relationship between the center of the window and the surrounding pixels, and s ═ x1，x2，z(x1，x2)}，z(x1，x2) Is a pixel (x)1，x2) The gray value of (a);
step two: an affine matrix is constructed, the performance of the saliency map is improved,
the similarity between two regions is represented by the distance between the structural local adaptive recursive kernels of the two regions, and the relevance w of the structural local adaptive recursive kernel in a region m and the structural local adaptive recursive kernel in another region nmnEquation (5) is:
Figure GDA0003333787230000055
wherein, sl ism，slnRespectively representing the junctions of the regions m, nMean of a constitutive locally adaptive recursive kernel, Q (n), representing a set of neighborhoods of the region n, σ1Is a parameter for controlling the degree of similarity, MCS () represents a cosine similarity matrix,
then, a row-normalized affine matrix is constructed, with equation (6) as:
A＝D-1·W (6)
wherein the affine matrix W ═ Wmn]N×NUsed to represent the similarity between any pair of nodes, the angle matrix D ═ diag { D }1，d2，...，dNIn which d isn＝∑nwmnRepresents the sum of the relevance of the region n to all other regions,
based on a given affine matrix, the saliency of a local region is defined by a descriptor of a structural local adaptive recursive kernel, and the formula (7) is as follows:
Figure GDA0003333787230000061
wherein, amnRepresents the degree of association, S, calculated in equation (6)SLARKRepresenting a low-quality saliency map based on structural locally adaptive recursive kernels, N representing the number of structural locally adaptive recursive kernels;
step three: based on the Gaussian mixture model, global constraints are established,
first, a global condition is defined and its cost is minimized, and equation (8) is:
Figure GDA0003333787230000062
wherein, b1，b2A gaussian mixture model representing the foreground and background in the infrared image respectively,
the descriptor of each structural locally adaptive recursive kernel can be regarded as a weight combined with a gaussian mixture model, and belongs to the probability of a neighborhood, and formula (9) is:
Figure GDA0003333787230000063
wherein, PnRepresents a linear operation for extracting the nth structural local recursive kernel region from the infrared image, wmnIs calculated by the formula (5) to obtainmIs a covariance matrix, Φ represents a gaussian distribution;
step four: the structural filtering method is used for filtering noise interference,
based on a structural local adaptive recursive kernel, a filtering method is designed to further filter background noise contained in a gaussian model, and formula (10) is as follows:
Figure GDA0003333787230000064
wherein S isG(x2) Denotes SGMiddle pixel x2The value of the significance of (a) is,
Figure GDA0003333787230000065
is a normalization factor, R (x)2) Is represented by x1Are the pixels in the neighborhood of the center of the circle,
Figure GDA0003333787230000066
step five: integrating the local and global models, computing the final saliency map,
the formula (11) is:
S*＝αS1+(1-α)S2 (11)
wherein α is a balance factor, S1Representing a local saliency map, S2Representing a global saliency map.
As shown in fig. 2, the local saliency map obtained in step two in the present invention is shown, where (a) in fig. 2 is an input image, (b) in fig. 2 is a local saliency map, and (c) in fig. 2 is a local saliency map to which affine matrix calculation is added. The influence of the affine matrix is increased, so that the saliency map is more accurate, and the background noise is well suppressed.
As shown in FIG. 3, it is the formula (5) in step two
Figure GDA0003333787230000071
Local saliency maps at different values are taken. Wherein, in (a) of FIG. 3
Figure GDA0003333787230000072
In (b) of FIG. 3
Figure GDA0003333787230000073
In (c) of FIG. 3
Figure GDA0003333787230000074
In (d) in FIG. 3
Figure GDA0003333787230000075
As shown in fig. 4, a saliency map of the global model. Fig. 4 (a) is an input image, fig. 4 (b) is a global saliency map, and fig. 4 (c) is a global saliency map to which a structural filtering calculation is added. Through the processing of structural filtering, the target is highlighted, and the background noise is also suppressed.
As shown in FIG. 5, it is a formula (10) of the four middle of the steps
Figure GDA0003333787230000076
Local saliency maps at different values are taken. In (a) of FIG. 5
Figure GDA0003333787230000077
In (b) of FIG. 5
Figure GDA0003333787230000078
In (c) of FIG. 5
Figure GDA0003333787230000079
In (d) of FIG. 5
Figure GDA00033337872300000710
As shown in fig. 6, it is a significance detection result diagram of the invention.
Through the above description, it can be found that the infrared image saliency detection method based on global and local interaction can effectively estimate the visual saliency target information, improve the effective follow-up region for target tracking and target identification in the later period, reduce the search consumption of the machine vision algorithm, improve the operation efficiency of the algorithm, reduce the operation power consumption of hardware, improve the resource utilization rate of image signals, and provide effective image preprocessing support for the visual task in the later period.
The technical solutions of the present invention are fully described above, it should be noted that the specific embodiments of the present invention are not limited by the above description, and all technical solutions formed by equivalent or equivalent changes in structure, method, or function according to the spirit of the present invention by those skilled in the art are within the scope of the present invention.

Claims (1)

1. An infrared image significance detection method based on global and local interaction comprises the following steps:
the method comprises the following steps: a structural locally adaptive recursive kernel is computed,
let F denote the feature image extracted from the input image I, and equation (1) is:
F(x，y)＝Γ(I，x，y) (1)
where Γ () represents a multi-dimensional function that extracts image features,
using the position, gradient, brightness, LBP and HOG information of the infrared image as the features of the image, in the feature image F, each pixel is described as a 7-dimensional vector, and formula (2) is:
Figure FDA0003370975950000011
wherein (x, y) represents the position of the pixel,
Figure FDA0003370975950000012
representation imageEdge information of pixels, HOG () representing HOG features, Lu () representing luminance features, LBP () representing LBP features,
a certain region R of F in the characteristic image is described as a multi-dimensional covariance matrix CRThe formula (3) is:
Figure FDA0003370975950000013
wherein z isiDenotes all the feature points in the region R, where i ═ 1., k, μ denotes ziIs determined by the average value of (a) of (b),
the calculation method of the structural local self-adaptive recursive kernel has the following formula (4):
Figure FDA0003370975950000014
wherein l belongs to [12]，P2Denotes the total number of pixels in the local window, Δ x denotes the coordinate relationship between the center of the window and the surrounding pixels, and s ═ x1，x2，z(x1，x2)}，z(x1，x2) Is a pixel (x)1，x2) The gray value of (a);
step two: an affine matrix is constructed, the performance of the saliency map is improved,
the similarity between two regions is represented by the distance between the structural local adaptive recursive kernels of the two regions, and the relevance w of the structural local adaptive recursive kernel in a region m and the structural local adaptive recursive kernel in another region nmnEquation (5) is:
Figure FDA0003370975950000015
wherein, sl ism，slnDenotes the mean of the structural locally adaptive recursive kernels of the regions m, n, respectively, Ω (n) denotes a set of neighborhoods of the region n, σ1Is a parameter controlling the degree of similarity, MCS () represents a cosine similarity matrix,
then, a row-normalized affine matrix is constructed, with equation (6) as:
A=D-1·W (6)
wherein the affine matrix W ═ Wmn]N×NUsed to represent the similarity between any pair of nodes, the angle matrix D ═ diag { D }1，d2，...，dNIn which d isn＝∑nwmnRepresents the sum of the relevance of the region n to all other regions,
based on a given affine matrix, the saliency of a local region is defined by a descriptor of a structural locally adaptive recursive kernel, and formula (7) is:
Figure FDA0003370975950000021
wherein, amnRepresents the degree of association, S, calculated in equation (6)SLARKRepresenting a low-quality saliency map based on structural locally adaptive recursive kernels, N representing the number of structural locally adaptive recursive kernels;
step three: based on the Gaussian mixture model, global constraints are established,
first, a global condition is defined and its cost is minimized, and equation (8) is:
Figure FDA0003370975950000022
wherein, b1，b2A gaussian mixture model representing the foreground and background in the infrared image respectively,
the descriptor of each structural local adaptive recursive kernel is regarded as the weight combined with the Gaussian mixture model, and belongs to the probability of the neighborhood, and the formula (9) is:
Figure FDA0003370975950000023
wherein, PnRepresents a linear operation for extracting the nth structural local recursive kernel region from the infrared image, wmnThe method is calculated by a formula (5), Cm is a covariance matrix, and phi represents Gaussian distribution;
step four: the structural filtering method is used for filtering noise interference,
based on a structural local adaptive recursive kernel, a filtering method is designed to further filter background noise contained in a gaussian model, and a formula (10) is as follows:
Figure FDA0003370975950000031
wherein S isG(x2) Denotes SGMiddle pixel x2The value of the significance of (a) is,
Figure FDA0003370975950000032
is a normalization factor, R (x)2) Is represented by x1As pixels in the neighborhood of the center of the circle,
Figure FDA0003370975950000033
step five: integrating the local and global models, calculating the final saliency map,
the formula (11) is:
S*＝αS1+(1-α)S2 (11)
wherein α is a balance factor, S1Represents a local saliency map, S2Representing a global saliency map.
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