CN108734174A - A kind of complex background image conspicuousness detection method based on low-rank representation - Google Patents
A kind of complex background image conspicuousness detection method based on low-rank representation Download PDFInfo
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Abstract
The complex background image conspicuousness detection method based on low-rank representation that the invention discloses a kind of.By increasing the distance restraint between sparse subgraph in low-rank conspicuousness model, increases the gap of conspicuousness target and target context, priori weight is merged in sparse subgraph, to enhance conspicuousness target information in matrix decomposition.Conspicuousness target and the resolution capability of target context can be improved in the present invention, can be used for the image detection of large area conspicuousness target and background complexity.
Description
Technical field
The present invention relates to saliency detection fields, more particularly, to a kind of complex background based on low-rank representation
Image significance detection method.
Background technology
The standard of saliency detection is can to protrude object the most significant, consistent highlighted entire obvious object,
The boundary of object can accurately be met, while there is higher noise immunity.
Low-rank representation is a kind of method for capableing of capture images data low dimensional structures, it is assumed that the background characteristics of image belongs to same
One lower-dimensional subspace, and regard the conspicuousness target of reduced size as sparse noise, therefore utilize low-rank matrix recovery algorithms
The eigenmatrix of image can be decomposed into low-rank matrix and sparse matrix.
There are some conspicuousness detection algorithms restored based on low-rank matrix in the recent period.Conspicuousness inspection based on low-rank representation
Survey method in the application can be successful, but still has shortcoming, especially when the well-marked target cause not of uniform size in image
And when being influenced by complex background, existing detection method is vulnerable to numerous and disorderly color, complicated surface texture and changeable background
Influence.
Invention content
It is an object of the invention to realize that sparse subgraph divides by spectral clustering, it is notable to increase sparse subgraph bound term enhancing
The spatial structural form of feature, addition Image Priori Knowledge fusion further increases detectability, and is decomposed by low-rank matrix
Well-marked target matrix is obtained, the notable figure of image is finally calculated.
To achieve the above object, technical scheme is as follows：
A kind of complex background image conspicuousness detection method based on low-rank representation, which is characterized in that include the following steps：
Step S1：Input picture is split using superpixel segmentation method, the input picture after segmentation is not weighed by n blocks
Folded super-pixel P={ P1,P2,…,PnComposition, feature extraction is carried out to the input picture after segmentation and obtains eigenmatrix F；
Step S2：The graph structure G of input picture after structure segmentation, and the dilute of graph structure G is obtained by Spectral Clustering
Dredge subgraph N；
Step S3：It defines and solves SDSC (Saliency Detection based on low rank
Representation with Subgraph Constraint) low-rank model, priori is incorporated by the form of weight
Into SDSC low-rank models, SDSC low-rank models are：
Step S4：The saliency value of each super-pixel is calculated using the sparse matrix S being calculated in step S3：
Sal(Pj)=| | Sj||1
In above step, n is the number of input picture super-pixel, and L is low-rank matrix, and S is sparse matrix, NiIndicate dilute
I-th piece of sparse subgraph block in subgraph N is dredged,It is the submatrix of matrix S, with sparse subgraph block NiIt is corresponding, ωiIt represents dilute
Dredge subgraph block NiIt is the prior probability of well-marked target, α is model parameter,For matrix nuclear norm,For p norms, 1≤p<
∞。
Further, in the step S1, it includes that extraction includes RGB color, is saturated to carry out feature extraction to input picture
The color characteristic of degree and coloration；Utilize 4 direction texture feature extractions in 3 channel of Gabor filter filters pair；It utilizes
4 directions in 3 channel of Steerable pyramids filters pair are filtered, and extract edge feature.
Further, in the step S3, ωiPriori includes location-prior, color priori and background priori.
Further, in the step S3, for j-th of super-pixel Pj, priori τj∈ [0,1], represents super
Pixel PjThe probability for being well-marked target is τj, then sparse subgraph block NiPrior probability be：
ωi=1-max ({ τj:Pj∈Ni})。
Further, in the step S3, the method for solving SDSC low-rank models is alternating direction multipliers method, including following
Step：
Step S31：Input parameter, including eigenmatrix F, model parameter α, sparse subgraph N and sparse subgraph block Ni's
Prior probability ωi；
Step S32：Initialization calculates, and defines cyclic variable k=0, procedure parameter L0=0, S0=0, Y0=0, μ0=0.1,
μmax=1010, ρ=1.1, convergence criterion ε；
Step S33：If | | F-Lk-Sk||p>ε then enters step S34, otherwise enters step S35；
Step S34：Following calculate is carried out successively：
Lk+1=argminLL(L,Sk,Yk,μk)
Sk+1=argminSL(Lk+1,S,Yk,μk)
Yk+1=Yk+μk(F-Lk+1-Sk+1)
μk+1=min (μk,ρμk)
K=k+1
Complete return to step S33 after calculating；
Step S35：Current Lk, SkFor result of calculation.
Further, in the step S34, in kth step, L is solvedk+1Method be,
Lk+1=argminLL(L,Sk,Yk,μk)
=UTδ[Σ]VT
WhereinU, Σ, V are ZLSingular value decomposition as a result, (U, Σ,
V)=SVD (ZL), Σ is ZLSingular value matrix, be a positive semidefinite diagonal matrix, element si, Tδ[Σ]=diag
({max(si-δ,0)})。
Further, in the step S34, in kth step, S is solvedk+1Method be,
WhereinSolution procedure is：
Step S71：Enable S=ZS, cyclic variable i=1；
Step S72：Following calculation is executed, until cyclic variable i=n,
Step S73：S is obtained by row combinationk+1；
In above step, NiIndicate i-th piece of sparse subgraph block in sparse subgraph N,It is the submatrix of matrix S, with
Sparse subgraph block NiIt is corresponding, ωiRepresent subgraph NiIt is the prior probability of well-marked target, α is model parameter,For matrix F-
Norm,For p norms, 1≤p<∞.
It can be seen from the above technical proposal that the present invention by between increasing sparse subgraph in low-rank conspicuousness model away from
From constraint, increase the gap of conspicuousness target and target context, helps to detect large area conspicuousness target and background complexity etc.
Problem merges priori weight in sparse subgraph, to enhance conspicuousness target information in matrix decomposition.Therefore, this hair
It is bright that there is the resolution capability for improving conspicuousness target and target context, it can be used for the figure of large area conspicuousness target and background complexity
As the distinguishing feature of detection.
Description of the drawings
Fig. 1 is conspicuousness target detection flow chart of the present invention；
Fig. 2 is SOD conspicuousnesses testing result figure in the embodiment of the present invention；
Fig. 3 is ECSSD conspicuousnesses testing result figure in the embodiment of the present invention.
Specific implementation mode
Below in conjunction with the accompanying drawings, the specific implementation mode of the present invention is described in further detail.
It should be noted that in following specific implementation modes, when embodiments of the present invention are described in detail, in order to clear
Ground indicates the structure of the present invention in order to illustrate, spy does not draw to the structure in attached drawing according to general proportion, and has carried out part
Amplification, deformation and simplified processing, therefore, should avoid in this, as limitation of the invention to understand.
In specific implementation mode of the invention below, please refer to Fig.1.As shown,
A kind of complex background image conspicuousness detection method based on low-rank representation, which is characterized in that include the following steps：
Step S1：Input picture is split using superpixel segmentation method, the input picture after segmentation is not weighed by n blocks
Folded super-pixel P={ P1,P2,…,PnComposition, feature extraction is carried out to the input picture after segmentation and obtains eigenmatrix F.
The present invention removes the color characteristic of description image using RGB color feature, saturation degree and coloration；Utilize Gabor
4 direction texture feature extractions in 3 channel of filter filters pair；Utilize 3 channel of Steerable pyramids filters pair
4 directions be filtered, extract the edge feature of image.
Utilize super-pixel segmentation construction feature matrix F.
Step S2：The graph structure G of input picture after structure segmentation, and the dilute of graph structure G is obtained by Spectral Clustering
Dredge subgraph N.
The Laplacian Matrix L=D-W of graph structure G, wherein W are the adjacency matrix of graph structure G, and D is the degree square of matrix W
The division of battle array, sparse subgraph is obtained by carrying out spectral clustering to L.By the corresponding feature vector of preceding k minimal eigenvalue of L according to
The direction of row forms the matrix of a n × k, is clustered to obtain graph structure G by carrying out K-means clustering algorithms to the matrix
Sparse subgraph N, every n super-pixel data block of the belonging classification of row, that is, initial belonging classification respectively in result.
Step S3：It defines and solves SDSC (Saliency object detection based on matrix low
Rank sparse decomposition) low-rank model, priori is dissolved by SDSC low-rank models by the form of weight
In.
By adding priori value bound term in sparse subgraph, the conspicuousness detectability of model can be improved.The present invention will
Location-prior, color priori and background priori are merged, and obtain the priori of pixel, and by the form of weight by priori
Knowledge is dissolved into model.
For j-th of super-pixel Pj, priori τj∈ [0,1], represents super-pixel PjIt is the probability of well-marked target
It is τj, then sparse subgraph block NiPrior probability be
ωi=1-max ({ τj:Pj∈Ni})
SDSC low-rank models are：
The method for solving SDSC low-rank models is alternating direction multipliers method ADMM (Alternating Direction
Method of Multipliers), include the following steps：
Step S31：Input parameter, including eigenmatrix F, model parameter α, sparse subgraph N, sparse subgraph block NiPriori
Probability ωi；
Step S32：Initialization calculates, and defines cyclic variable k=0, procedure parameter L0=0, S0=0, Y0=0, μ0=0.1,
μmax=1010, ρ=1.1, convergence criterion ε；
Step S33：If | | F-Lk-Sk||p>ε then enters step S34, otherwise enters step S35；
Step S34：Following calculate is carried out successively：
Lk+1=argminLL(L,Sk,Yk,μk)
Sk+1=argminSL(Lk+1,S,Yk,μk)
Yk+1=Yk+μk(F-Lk+1-Sk+1)
μk+1=min (μk,ρμk)
K=k+1
Complete return to step S33 after calculating；
Step S35：Current Lk, SkFor result of calculation.
In step S34, in kth step, L is solvedk+1Method be,
Lk+1=argminLL(L,Sk,Yk,μk)
=UTδ[Σ]VT
WhereinU, Σ, V are ZLSingular value decomposition as a result, (U, Σ,
V)=SVD (ZL), Σ is ZLSingular value matrix, be a positive semidefinite diagonal matrix, wherein siIndicate its diagonal entry, Tδ
[Σ]=diag ({ max (si-δ,0)})。
In step S34, in kth step, S is solvedk+1Method be,
WhereinSolution procedure is
Step S71：Enable S=ZS, cyclic variable i=1；
Step S72：Following calculation is executed, until cyclic variable i=n,
Step S73：S is obtained by row combinationk+1。
In above-mentioned steps,For matrix F-norm.
Step S4：The saliency value of each super-pixel is calculated using the sparse matrix S being calculated in step S3
Sal(Pj)=| | Sj||1
SjRepresent the jth row of sparse matrix S, saliency value Sal (Pj) bigger, show PjBe well-marked target probability it is bigger.
The effect of foregoing invention content is verified with specific example.
Using C++ and MATLAB hybrid programmings, the experimental situation used is for the realization of algorithm：Operating system is
Windows7, CPU frequency 2.40GHz, running memory 4GB, single threading environment.
The present invention tests on SOD data sets and ECSSD data sets, is wherein to have multiple show in SOD data sets
Target is write, and the image in ECSSD data sets then has more complicated background.Utilize 8 kinds of conspicuousnesses of mainstream in recent years
Detection algorithm is compared with method proposed by the present invention, is specifically included：CB, GS, MR, PCA, RBD, SDB, SF, ULR, algorithm
It is provided by author official, specific parameter also all uses default parameters.
The parameter setting of SDSC models is as follows：The block n=250 for enabling super-pixel segmentation enables cluster number k in the spectral clustering stage
=53；In the matrix decomposition stage, σ is enabled2=0.05, model parameter α=0.35.
The present invention carries out the assessment of well-marked target model, including AUC value, F- using 4 universally recognized evaluation indexes
Measure (WF) values and mean absolute error MAE, Duplication OR.
Algorithm testing result figure in SOD and ECSSD data sets is respectively shown in Fig. 2, Fig. 3, is had for the two multiple
The data set of miscellaneous background, this method can obtain good performance results.
The result that indices in SOD data sets and ECSSD data sets are compared is as shown in Table 1 and Table 2, and table 1 can
To find out that tri- indexs of WF, AUC and OR of model are all in first position, MAE comes the position of third；Table 2 indicates AUC
Index comes on first position, and excess-three item index MAE, WF, OR are come on second position.Statistics indicate that this method
Comprehensive performance be optimal.
Table 1：SOD data set result tables
Table 2：ECSSD data set result tables
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Any one skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (7)
1. a kind of complex background image conspicuousness detection method based on low-rank representation, which is characterized in that include the following steps：
Step S1：Input picture is split using superpixel segmentation method, the input picture after segmentation is nonoverlapping by n blocks
Super-pixel P={ P1,P2,…,PnComposition, feature extraction is carried out to the input picture after segmentation and obtains eigenmatrix F；
Step S2：The graph structure G of input picture after structure segmentation, and pass through the sparse son of Spectral Clustering acquisition graph structure G
Scheme N；
Step S3：SDSC low-rank models are defined and solved, priori is dissolved by SDSC low-rank models by the form of weight
In, SDSC low-rank models are：
Step S4：The saliency value of each super-pixel is calculated using the sparse matrix S being calculated in step S3：
Sal(Pj)=| | Sj||1
In above step, n is the number of input picture super-pixel, and L is low-rank matrix, and S is sparse matrix, NiIndicate sparse son
Scheme i-th piece of sparse subgraph block in N,It is the submatrix of matrix S, with sparse subgraph block NiIt is corresponding, ωiRepresent sparse son
Segment NiIt is the prior probability of well-marked target, α is model parameter,For matrix nuclear norm,For p norms, 1≤p<∞.
2. according to the method described in claim 1, it is characterized in that, in the step S1, feature extraction is carried out to input picture
Include the color characteristic of RGB color, saturation degree and coloration including extraction；Utilize 4 of 3 channel of Gabor filter filters pair
Direction texture feature extraction；It is filtered using 4 directions in 3 channel of Steerable pyramids filters pair, extracts side
Edge feature.
3. according to the method described in claim 1, it is characterized in that, in the step S3, ωiPriori include location-prior,
Color priori and background priori.
4. according to the method described in claim 1, it is characterized in that, in the step S3, for j-th of super-pixel Pj, priori
Knowledge is τj∈ [0,1], represents super-pixel PjThe probability for being well-marked target is τj, then sparse subgraph block NiPrior probability be：
ωi=1-max ({ τj:Pj∈Ni})。
5. according to the method described in claim 1, it is characterized in that, in the step S3, the method that solves SDSC low-rank models
For alternating direction multipliers method, include the following steps：
Step S31：Input parameter, including eigenmatrix F, model parameter α, sparse subgraph N and sparse subgraph block NiPriori it is general
Rate ωi；
Step S32：Initialization calculates, and defines cyclic variable k=0, procedure parameter L0=0, S0=0, Y0=0, μ0=0.1, μmax=
1010, ρ=1.1, convergence criterion ε；
Step S33：If | | F-Lk-Sk||p>ε then enters step S34, otherwise enters step S35；
Step S34：Following calculate is carried out successively：
Lk+1=argminLL(L,Sk,Yk,μk)
Sk+1=argminSL(Lk+1,S,Yk,μk)
Yk+1=Yk+μk(F-Lk+1-Sk+1)
μk+1=min (μk,ρμk)
K=k+1
Complete return to step S33 after calculating；
Step S35：Current Lk, SkFor result of calculation.
6. according to the method described in claim 5, it is characterized in that, in the step S34, in kth step, L is solvedk+1Method
For,
Lk+1=argminLL(L,Sk,Yk,μk)
=UTδ[Σ]VT
WhereinU, Σ, V are ZLSingular value decomposition as a result, (U, Σ, V)=
SVD(ZL), Σ is ZLSingular value matrix, be a positive semidefinite diagonal matrix, element si, Tδ[Σ]=diag ({ max
(si-δ,0)})。
7. according to the method described in claim 1, it is characterized in that, in the step S34, in kth step, S is solvedk+1Method
For,
WhereinSolution procedure is：
Step S71：Enable S=ZS, cyclic variable i=1；
Step S72：Following calculation is executed, until cyclic variable i=n,
Step S73：S is obtained by row combinationk+1；
In above step, NiIndicate i-th piece of sparse subgraph block in sparse subgraph N,It is the submatrix of matrix S, and it is sparse
Subgraph block NiIt is corresponding, ωiRepresent subgraph NiIt is the prior probability of well-marked target, α is model parameter,For matrix F-norm,For p norms, 1≤p<∞.
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| CN111046868A (en) * | 2019-11-26 | 2020-04-21 | 广东石油化工学院 | Target significance detection method based on matrix low-rank sparse decomposition |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110503113A (en) * | 2019-08-28 | 2019-11-26 | 江苏建筑职业技术学院 | A kind of saliency object detection method restored based on low-rank matrix |
| CN110503113B (en) * | 2019-08-28 | 2023-07-28 | 江苏建筑职业技术学院 | Image saliency target detection method based on low-rank matrix recovery |
| CN111046868A (en) * | 2019-11-26 | 2020-04-21 | 广东石油化工学院 | Target significance detection method based on matrix low-rank sparse decomposition |
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