CN104715476B - A kind of well-marked target detection method based on histogram power function fitting - Google Patents
A kind of well-marked target detection method based on histogram power function fitting Download PDFInfo
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Abstract
A kind of well-marked target detection method based on histogram power function fitting, including the classification of histogram power function fitting, super-pixel, marking area problem and well-marked target detect four steps.Beneficial effect of the present invention：Using FT notable figures, figure manifold ranking method, SLIC methods super-pixel classify, histogram power function fitting ask gray threshold image multiple target and scene complexity in the case of, detection efficiency is high, performance is good, high precision, solve a great problem of well-marked target detection field, and method provided by the present invention is performed, and speed is fast, algorithm complex is low, while can guarantee that accuracy of detection higher.
Description
Technical field
It is specifically a kind of aobvious based on histogram power function fitting the present invention relates to image well-marked target detection field
Write object detection method.
Background technology
It is well known that computing power and function are developed rapidly as machine intelligence provides reliable feasible condition, with
Going deep into for the subjects such as machine learning, pattern-recognition, people increasingly wish that computer can be more autonomous more intelligent complete
Into task.This target is realized, it is necessary to computer is it will be appreciated that the environment of surrounding.The main mode of human perception external information
It is by vision, so the key of computer understanding surrounding environment is with visually-perceptible disposal ability.
Well-marked target is the target that people pay close attention to the most in image, generally comprises more people interested, more useful
Information.Therefore, well-marked target detection is widely used in the fields such as target identification, image segmentation, image retrieval.Conventional notable mesh
Mark detection technique mainly has the marking area detection technique based on local contrast, such as：Based on local contrast and fuzzy growth technology,
Multiple dimensioned center-surrounding histogram and Color-spatial distribution correlation technique etc.；And the marking area detection based on global contrast
Technology, such as：Achanta proposes a kind of method of the marking area detection based on global contrast from frequency domain angle
（Frequency-tuned salient region detection, abbreviation FT methods）, the method will be by Gaussian low pass
The Euclidean distance between each pixel value and the average pixel value of entire image in ripple image as the point saliency value.
But can fail in the following two cases：
（1）The major part that the color of marking area is accounted in image, after the method is calculated, background can have higher showing
Work value；
（2）Containing a small amount of prominent color in background, the saliency value of this part colours in such background also can be very high.
Pertinent literature：ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned
salient region detection[C] // IEEE Conference on Computer Vision and Pattern
Recognition, 2009：1597–1604.
Although additionally, performance of the current many well-marked target detection models under single well-marked target and simple background scene is
Close to the standard of test set, but under multiple target and complex background, can not especially be obtained under the background that target is mutually melted preferably
Performance.
Figure manifold ranking（Graph Based Manifold Ranking）It is a kind of recent clustering method for occurring, passes through
The adjacency matrix and degree matrix of calculating figure obtain Laplacian regularizations or non-regularization matrix, and different variants can be applicable to
Under different environment.Figure manifold ranking is applied to well-marked target detection by Chuan Yang et al., and image is carried out into SLIC segmentations,
Super-pixel after segmentation using image border node as the seed of dependence query detecting background, then is negated as figure node
Difference obtains marking area.Detection results of this method under single goal and simple background are relatively good, but, when well-marked target position
In the case that image border, multiple target scene, background complexity or prospect are mutually melted with background, Detection results are not ideal enough.
Pertinent literature：Chuan Yang, Lihe Zhang, Huchuan Lu, Minghsuan Yang, Saliency
Detection via Graph-Based Manifold Ranking, CVPR2013, P3166-3173.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of well-marked target detection based on histogram power function fitting
Method, a gray threshold is found by the histogram data for calculating the notable figure that FT algorithms are obtained, and the threshold value can extract category
In the super-pixel in well-marked target region, and using these super-pixel as figure manifold ranking inquiry seed, then by self adaptation two
Value method extracts the super-pixel that there may exist notable pixel, as the supplement of inquiry seed, realizes close to test set standard
Notable figure, so as to realize being pointed to the more difficult detections such as image border, multiple target scene, background be complicated or prospect is mutually melted with background
Well-marked target detection.
The present invention is for the solution technical scheme that is used of above-mentioned technical problem：It is a kind of based on histogram power function fitting
Well-marked target detection method, it is characterised in that：Described object detection method is comprised the following steps：
Step one：Histogram power function fitting：Original image with FT algorithms is generated into FT notable figures and to be calculated this notable
The intensity histogram diagram data of figure, tries to achieve FT notable according to intensity histogram diagram data least square fitting power function curve equation
It is used for the gray threshold x of super-pixel classification in figure0；
Step 2：Super-pixel is classified：Original image is divided into n super-pixel with SLIC algorithms, is obtained according to step one
Super-pixel is divided into notable super-pixel and background super-pixel by gray threshold；
Step 3：Marking area is positioned：Find out the super-pixel that there is notable pixel；
Step 4：Well-marked target is detected：Super-pixel correlation matrix is calculated with each super-pixel with figure manifold ranking method
Relevance ranking value, and the significance of each super-pixel is obtained by the way that the relevance ranking value of each super-pixel is normalized, will
The significance of each super-pixel is assigned to its all pixels for including and generates final notable figure.
The method of gray threshold is asked to be with least square fitting power function curve equation in step one of the present invention：Root
According to the intensity histogram diagram data least square fitting power function curve equation of FT notable figures；The power function curve side that will be obtained
Journey derivation, by the point that derivative is -1（x0,y0）As the flex point of background gray scale and notable gray scale；x0Separated as in FT notable figures
The gray threshold of background and well-marked target；
It is by the method that super-pixel is divided into notable super-pixel and background super-pixel in step 2 of the present invention：
First, all pixels average gray mean_gray (i) of same super-pixel i are belonged in calculating FT notable figures;
2nd, all super-pixel are generated one by number and indicates vector Y1=[y1, y2 ... yn] T, by all super-pixel
Average gray mean_gray (i) is classified as notable super-pixel more than gray threshold, and (i=1,2 ... n) are set to 1, otherwise to its value yi
Background super-pixel is classified as, (i=1,2 ..., n) are set to 0 to its value yi;
The method of marking area positioning is in step 3 of the present invention：
First, by FT notable figure binaryzations：It is higher than 2 times of FT notable figures average gray gray scale using self-adaption binaryzation method
The pixel grey scale of value is set to 255, and gray scale is set to 0 less than the pixel grey scale of the gray scale；
2nd, all super-pixel are generated one by number and indicates vector Y2=[y1,y2,…yn]T, count the notable binary maps of FT
Middle gray scale is the super-pixel numbering belonging to 255 pixel, and the indicated value yi of the super-pixel i that there is notable pixel is set to 1, remaining
It is set to 0.
The computational methods of super-pixel correlation matrix are in step 4 of the present invention：Super-pixel after original image is split
Composition figure G=(V, E), wherein V represents all super-pixel set of figure G, and E represents the full connection line set of all nodes, is flowed with figure
Shape ranking method calculates super-pixel correlation matrix C=(D- α W)-1, wherein, D is the degree matrix of figure G, and W is the adjacent square of super-pixel
Battle array, α is coefficient correlation.
The computational methods of the relevance ranking value of each super-pixel are in step 4 of the present invention：
First, all super-pixel are generated one by number and indicates vector Y=[y1,y2,…yn]T, make Y=Y1.| Y2, i.e. Y takes
Y1And Y2Step-by-step or the value of computing；
2nd, according to formula f*=（D-αW）-1Y tries to achieve the relevance ranking value of each super-pixel.
The super-pixel number n that step 2 original image of the present invention is split by SLIC algorithms is 180-230.
The beneficial effects of the invention are as follows：（1）Target, the back of the body are calculated by histogram data power function fitting using FT notable figures
The method of scape segmentation threshold obtains notable super-pixel, and well-marked target all regions that may be present are positioned with the notable binary maps of FT,
Improve the precision of well-marked target detection；（2）Well-marked target detection is carried out with figure manifold ranking method, can quickly be realized in monocular
Mark, the simple scenario of simple background and multiple target, the well-marked target detection of complex scene and the binary map segmentation of marking area, enter
One step ensure that the precision of target detection, compensate for FT algorithms failure conditions that may be present；（3）FT notable figure combination figure manifolds
Sequence detects that, algorithm complex faster than the execution speed of most of well-marked target detection methods is low, while can guarantee that inspection higher
Survey precision；（4）Super-pixel classification involved in the present invention uses the preferable pixel cluster technology of forward position performance --- SLIC methods, gathers
Super-pixel after class can effectively preserve well-marked target edge, it is ensured that the notable figure for ultimately producing can more visible ground display target
Profile；（5）Marking area positioning involved in the present invention employs the binary map that FT notable figure self-adaption binaryzation methods are obtained
With reference to the method for super-pixel information, can to greatest extent ensure that notable pixel region is positioned out, be effectively improved
The precision of detection；（6）Well-marked target detection involved in the present invention employs the figure manifold ranking method of similar Spectral Clustering, non-
Since the laplacian matrixes of canonical can effectively search the node related to oneself inquiring about seed in figure node, and
It is ranked up according to correlation size so that notable figure formation speed is fast, high precision；（7）Histogram power involved in the present invention
It is a kind of error theory based on least square method that Function Fitting seeks grey relevant dynamic matrix, belongs to numerical analysis category, is not simple
Digital image processing techniques.（8）In the case of image multiple target and scene complexity, detection efficiency is high, and performance is good for the present invention,
High precision, solves a great problem of well-marked target detection field.
Brief description of the drawings
Fig. 1 is general flow chart of the present invention；
Fig. 2 seeks gray threshold flow chart for power function fitting of the present invention；
Fig. 3 is super-pixel classification process figure of the present invention；
Fig. 4 is marking area positioning flow figure of the present invention；
Fig. 5 is well-marked target overhaul flow chart of the present invention based on figure manifold ranking；
Fig. 6 is notable figure binaryzation flow chart of the present invention；
Fig. 7 is basic procedure instance graph of the present invention.
Specific embodiment
A kind of well-marked target detection method based on histogram power function fitting of the present invention, including：Histogram power letter
The steps such as number fitting, super-pixel classification, marking area positioning, well-marked target detection.
The specific implementation of the well-marked target detection method based on histogram power function fitting, now ties to illustrate the invention
Close embodiment and accompanying drawing is described below：
Fig. 1 is general flow chart of the invention, and it realizes the notable mesh based on histogram power function fitting by following steps
A complete procedure of detection method is marked, including：
Step one：The FT notable figures of original image are generated using FT algorithms, the notable diagram data FTimname of FT are according to formulaIts histogram data hist (i) is calculated after normalization,
Wherein i=0,1,2 ..., 255, hist (i) represents that gray scale is the number of pixels of i；
Step 2：It is before fitting that FT notable figures is straight to eliminate low gray scale high frequency time to the adverse effect for asking flex point to bring
Square figure number of pixels data hist (i) is all divided by 100（0≤i≤255）, then by least square method, use data set
(i, hist (i)) | and 0≤i≤255 } fitting power function curve y=AxB, obtain coefficient A and B；
Step 3：To power function y=AxBDerivation, obtains dy/dx=A*BxB-1, make dy/dx=-1 solve x0, by x0As super picture
The gray threshold of element classification；
Step 4：Original image is divided into n using SLIC algorithms（The value of n is 180-230）Super-pixel, obtains artwork
Attaching information matrix superpixels, superpixels (i, j) denotation coordination of pixel ownership super-pixel is the pixel of (i, j)
Affiliated super-pixel numbering；
Step 5：It is the one-dimensional vector Y of super-pixel number n to define a length1= [y1, y2, . . . , yn]T, meter
Belong to all pixels average gray mean_gray of same super-pixel, and and x in calculation FT notable figures0Compare, average value is big
In x0Super-pixel i be considered as notable super-pixel, yiValue be set to 1, be otherwise set to 0；
Step 6：FT notable figure binaryzations, gray scale are set to 255 more than the pixel grey scale of 2 times of average gray of FT notable figures,
Remaining is set to 0.It is the one-dimensional vector Y of super-pixel number n to define a length2= [y1, y2, . . . , yn]T, by all two
Gray scale is the indicated value y of the 255 affiliated super-pixel i of pixel in value figurei1 is set to, other are set to 0；
Step 7：Super-pixel is considered as node composition figure G=（V, E）, wherein V represents that the node set of figure G is i.e. all super
Pixel set, E represents the full connection line set of all nodes.First ask super-pixel adjacency matrix W, W each element w, ci and
Cj represents color averages of the super-pixel i and j in CIELAB color spaces, and σ is weight coefficient, can use the constant between 0 to 1, this
Invention takes 0.1, according to formulaThe adjacent weights of each node are calculated, the adjacency matrix of figure G is obtained, according to public affairs
FormulaTry to achieve the degree matrix D=diag { d of figure G11, . . . , dnn, if α is coefficient correlation, can use 0 to 1
Between constant, the present invention takes α=0.99, according to formula（D-αW）-1Try to achieve the correlation matrix of figure G；
Step 8：The notable instruction vector sum positioning that will be obtained in step 5 and step 6 indicates vector to do step-by-step or computing
Merge Y=Y1.|Y2, obtain indicating vectorial Y as inquiry, according to formulaTry to achieve each super-pixel
Significance, according to formulaNormalization significance, by the significance after normalization
The all pixels that super-pixel is included are assigned to, final notable figure is generated；
Step 9:According to formulaNotable figure binaryzation is aobvious so as to obtain
Write region binarization segmentation figure.
The mode of least square method power function fitting is as follows described in step 2：
Known histogram data point (i, hist (i)), wherein i=1,2 ..., m is distributed substantially one power function curve.Make fitting two
The power function curve of ginseng, it is convenient to solve, willBoth sides are taken the logarithm, order；
The curve is not, by all of data point, but to make sum of square of deviationsFor most
It is small, wherein every group of data are with the deviation of matched curve；
According to the principle of least square, should take A and B makesThere is minimum, therefore A and B should meet following condition：
Obtain final product following normal equation group
Equation group is solved, A and B is solved, substituted intoObtain final product fitting power function curve.
Claims (4)
1. a kind of well-marked target detection method based on histogram power function fitting, it is characterised in that：Described target detection side
Method is comprised the following steps：
Step one：Histogram power function fitting：By original image is with FT algorithms generation FT notable figures and is calculated the notable figure
Intensity histogram diagram data, in trying to achieve FT notable figures according to intensity histogram diagram data least square fitting power function curve equation
For the gray threshold x of super-pixel classification0；
Wherein the specific method of gray threshold is asked to be with least square fitting power function curve equation：According to grey level histogram number
According to least square fitting power function curve equation, the power function curve equation derivation that will be obtained, by the point that derivative is -1
（x0,y0）As the flex point of background gray scale and notable gray scale, x0As separating background in FT notable figures and the gray scale threshold of well-marked target
Value；
Step 2：Super-pixel is classified：Original image is divided into n super-pixel with SLIC algorithms, according to the gray scale that step one is obtained
Super-pixel is divided into notable super-pixel and background super-pixel by threshold value, and its specific method is：
First, all pixels average gray mean_gray (i) of same super-pixel i are belonged in calculating FT notable figures;
2nd, all super-pixel are generated one by number and indicates vector Y1=[y1,y2,…yn]T, by average ash in all super-pixel
Degree mean_gray (i) is classified as notable super-pixel more than gray threshold, its value yi(i=1,2 ..., n) are set to 1, are otherwise classified as the back of the body
Scape super-pixel, its value yi(i=1,2 ..., n) it is set to 0；
Step 3：Marking area is positioned：The super-pixel that there is notable pixel is found out, specific method is：
First, by FT notable figure binaryzations：Using self-adaption binaryzation method gray scale higher than 2 times of values of FT notable figures average gray
Pixel grey scale is set to 255, is otherwise set to 0；
2nd, all super-pixel are generated one by number and indicates vector Y2=[y1,y2,…yn]T, ash in the statistics notable binary maps of FT
The super-pixel numbering belonging to 255 pixel is spent, the indicated value yi of the super-pixel i that there is notable pixel is set to 1, be otherwise set to
0；
Step 4：Well-marked target is detected：It is related to each super-pixel super-pixel correlation matrix to be calculated with figure manifold ranking method
Property ranking value, and the significance of each super-pixel is obtained by the way that the relevance ranking value of each super-pixel is normalized, by each
The significance of super-pixel is assigned to its all pixels for including and generates final notable figure.
2. a kind of well-marked target detection method based on histogram power function fitting according to claim 1, its feature exists
In：The computational methods of super-pixel correlation matrix are in the step 4：After original image is split super-pixel composition figure G=(V,
E), wherein V represents all super-pixel set of figure G, and E represents the full connection line set of all nodes, with figure manifold ranking method meter
Calculate super-pixel correlation matrix C=(D- α W)-1, wherein, D is the degree matrix of figure G, and W is the adjacency matrix of super-pixel, and α is phase relation
Number.
3. a kind of well-marked target detection method based on histogram power function fitting according to claim 1, its feature exists
In：The computational methods of the relevance ranking value of each super-pixel are in the step 4：
First, all super-pixel are generated one by number and indicates vector Y=[y1,y2,…yn]T, make Y=Y1.| Y2, i.e. Y takes Y1With
Y2Step-by-step or the value of computing；
2nd, according to formula f*=（D-αW）-1Y tries to achieve the relevance ranking value of each super-pixel.
4. a kind of well-marked target detection method based on histogram power function fitting according to claim 1, its feature exists
In：The super-pixel number n that the step 2 original image is split by SLIC algorithms is 180-230.
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| CN105761238B (en) * | 2015-12-30 | 2018-11-06 | 河南科技大学 | A method of passing through gray-scale statistical data depth information extraction well-marked target |
| CN105913064B (en) * | 2016-04-12 | 2017-03-08 | 福州大学 | A kind of image vision conspicuousness detection fitting optimization method |
| CN106056590B (en) * | 2016-05-26 | 2019-02-22 | 重庆大学 | Conspicuousness detection method based on Manifold Ranking and combination prospect background feature |
| CN106296695B (en) * | 2016-08-12 | 2019-05-24 | 西安理工大学 | Adaptive threshold natural target image segmentation extraction algorithm based on conspicuousness |
| CN106447699B (en) * | 2016-10-14 | 2019-07-19 | 中国科学院自动化研究所 | High iron catenary object detecting and tracking method based on Kalman filtering |
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