CN103268494A - Parasite egg identifying method based on sparse representation - Google Patents
Parasite egg identifying method based on sparse representation Download PDFInfo
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- CN103268494A CN103268494A CN2013101810124A CN201310181012A CN103268494A CN 103268494 A CN103268494 A CN 103268494A CN 2013101810124 A CN2013101810124 A CN 2013101810124A CN 201310181012 A CN201310181012 A CN 201310181012A CN 103268494 A CN103268494 A CN 103268494A
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Abstract
The invention belongs to the technical field of image identification, and particularly relates to a parasite egg identifying method based on sparse representation. The method comprises that steps of establishing an initial dictionary; using a K-SVD algorithm to study the dictionary; processing an input image; calculating a reconstructed error matrix; obtaining a candidate image block; identifying the candidate image block. The parasite egg identifying method based on sparse representation introduces a sorting algorithm based on sparse representation, reinforces robustness of the whole parasite egg algorithm to various interfering factors, introduces the Batch-OMP algorithm which is used in the process of large-scale sparse representation, improves identification efficiency, introduces a method that a dictionary is directly established after a sample is subjected to gaussian pyramid dimensionality reduction, avoids the step that egg target characteristics are abstracted, enables the identification process to be simple and convenient, introduces the method for establishing an error matrix and solving the smallest local value of the error matrix, and avoids the situation that different image blocks containing the same target are obtained in the preliminary locating process.
Description
Technical field
The invention belongs to the image recognition technology field, be specifically related to a kind of parasite egg recognition methods based on rarefaction representation.
Background technology
The key that is based upon the automatic identification of parasite egg on Computer Image Processing and the medical science microtechnic is to design image recognition algorithm fast and effectively, in the past based on the parasite egg automatic identifying method of image mainly by means of isolating the worm's ovum target earlier, extract the various features of target again, finish identification in conjunction with a sorter at last.Lift the two examples methods more relevant with the present invention: bending moment and support vector machine are not identified 16 kinds of parasite of human worm's ovums in document " An expert diagnosis system for classification of human parasite eggs based on multi-class SVM " in conjunction with 7 of Hu for people such as (1) Derya Avci 2009, though obtain very high discrimination, but only under the comparatively desirable prerequisite of image, just can reach, not consider disturbing factor situation more for a long time; (2) Chinese patent CN201110022426.3 has proposed a kind of method in conjunction with the parasite egg edge histogram parasite of human worm's ovum has been carried out shape recognition, overcome the influence of weak boundary preferably, improve the reliability of identification, yet still had deficiency for the comparatively similar parasitic shape recognition of shape.From existing method, the kind of feature is more, also comprise color, shape, size, texture etc. the feature of describing in said method, feature selecting gets the fine or not last discrimination that largely determined, simultaneously Primary Location target and the step of extracting feature are also finished accurately than difficulty.Sorter is also varied, comprise Bayes classifier, linear discriminant analysis, support vector machine, neural network, minor increment etc., because these sorters are responsive to feature, so selecting which kind of feature is that optimum often being difficult to determined for sorter, simultaneously these sorters to such as noise, block, the robustness of disturbing factor such as impurity all a little less than.
Application based on the rarefaction representation sorting algorithm also launches far away, such rudimentary algorithm framework is in different application scenarios, need transform and expand algorithm in conjunction with other technologies and skill, especially need to determine according to concrete needs aspect the selection of data dimensionality reduction, rarefaction representation algorithm, the dictionary study.Based on above analysis, be applied to first realize the identification of single class or multiclass parasite egg in the parasite egg identification problem.
Summary of the invention
Order of the present invention is to overcome parasite egg recognition methods in the past to feature and the more sensitive defective of various disturbing factor, in conjunction with the Batch-OMP algorithm and the K-SVD dictionary learning algorithm that are fit to extensive rarefaction representation, a kind of parasite egg recognition methods based on rarefaction representation has been proposed, on discrimination and recognition efficiency to satisfy the application demand of actual parasite egg automatic recognition system.
In order to realize the foregoing invention purpose, the present invention by the following technical solutions: a kind of parasite egg recognition methods based on rarefaction representation may further comprise the steps:
(1) set up initial dictionary: initial single category dictionary is set up in single class identification, and initial associating dictionary is set up in multiclass identification;
(2) dictionary study: use the K-SVD algorithm that dictionary is learnt, single class identification obtains single class and represents dictionary, and multiclass identification obtains uniting the expression dictionary and unites the differentiation dictionary;
(3) handle input picture: input picture is carried out pyramidal compression, with the mode of moving window compressed image is carried out piecemeal, step-length can be chosen as one or more pixels; The all images piece is carried out rarefaction representation, and the dictionary of single class identification adopts single class to represent dictionary, and the dictionary of multiclass identification adopts associating expression dictionary;
(4) calculate the reconstruction error matrix;
(5) obtain the candidate image piece: utilize the reconstruction error matrix that obtains in the step (4), seek its all local minimum, choose k the wherein minimum corresponding image block of value as candidate target;
(6) identification candidate image piece: for single class identification situation, differentiate the candidate image piece with threshold value, identification is finished; For multiclass identification situation, the candidate image piece is carried out rarefaction representation, use associating differentiation dictionary, calculate sub-dictionary reconstruction error, use threshold mode that the candidate image piece is differentiated and classification, identification is finished.
In the step (1), it is as follows to set up initial dictionary step:
(1) select the less and representative parasite egg image pattern cn of some impurity, wherein c is 〉=1 integer, represents the class number, and n represents the sample number of each class;
(2) adopt gaussian pyramid that cn width of cloth image is compressed, obtain the image pattern behind the dimensionality reduction;
(3) be spacing with the d degree, the every width of cloth image that obtains in the previous step is rotated a circle obtains 360/d image pattern (comprising former figure), so total sample number is N=360cn/d;
(4) each two-dimensional image data " elongation " that previous step is obtained is one-dimensional vector, again each vector is carried out standardization, makes each vector satisfy l
2-norm is 1;
(5) all standardized vectors that previous step is obtained obtain initial dictionary as the atom of dictionary, if c=1, what then obtain is initial single category dictionary of single class identification, if c＞1, what then obtain is the initial associating dictionary of multiclass identification, comprises c sub-dictionary.
In the step (2), use the K-SVD algorithm that dictionary is learnt, be divided into three kinds of situations:
(1) at single class parasite egg identification, with the K-SVD algorithm initial single category dictionary is learnt, obtained single class and represent dictionary, this dictionary is used for Primary Location and classification simultaneously, and the volume of dictionary is decided according to the dimension of former subvector;
(2) at the identification of multiclass parasite egg, with the K-SVD algorithm whole initial associating dictionary is learnt, obtained associating expression dictionary, this dictionary is used for Primary Location;
(3) at multiclass parasite egg identification, with the K-SVD algorithm each initial sub-dictionary is learnt, the sub-dictionary after all study is united obtain associating differentiation dictionary again, this dictionary is used for classifying, and its volume is much larger than uniting the volume of representing dictionary.
In the step (3), it is extensive rarefaction representation that all images piece is carried out rarefaction representation, namely uses Batch-OMP algorithm solution formula (1-1):
min||x-Dθ||
2s.t.||θ||
0≤T （1-1）
Wherein x is input signal, and D is that the single class that obtains in the step (2) is represented dictionary or united the expression dictionary, and θ is coefficient, and T is sparse property condition.
In the step (4), calculate the reconstruction error matrix, step is as follows:
(1) utilizes formula (1-2) to calculate reconstruction error, obtain the reconstruction error [e of all images piece
1, e
2... e
L], wherein L is the image block number;
(2) order of the image block that obtains according to step (3) is with [e
1, e
2... e
L] be arranged in a two-dimensional matrix according to the order of sequence, what obtain is the reconstruction error matrix.
In the step (6), calculate sub-dictionary reconstruction error, calculate according to formula (1-3).
D wherein
iBe associating dictionary D=[D
1, D
2... D
c] sub-dictionary, i=1 wherein, 2 ..., c, c are the class number.
The sorting algorithm based on rarefaction representation has been introduced in parasite egg recognition methods based on rarefaction representation of the present invention, has strengthened whole parasite egg recognizer to the robustness of various disturbing factors; Introduce the Batch-OMP algorithm and be used for extensive rarefaction representation process, improved recognition efficiency; Introduced the method for directly setting up dictionary with the sample behind the gaussian pyramid dimensionality reduction, avoided extracting the step of worm's ovum target signature, made identifying become more easy; Introduce the method for setting up error matrix and asking its local minimum, avoided in the Primary Location process, obtaining comprising the different images piece of same target.
Description of drawings
Fig. 1 is the process flow diagram that the present invention is based on the parasite egg recognition methods of rarefaction representation.
A few class parasite egg samples that Fig. 2 chooses.
The initial single category dictionary of Fig. 3.
Fig. 4 initially unites dictionary.
Fig. 5 list class is represented dictionary.
Fig. 6 unites the expression dictionary.
Fig. 7 unites the differentiation dictionary.
Fig. 8 input picture.
Fig. 9 reconstruction error vector.
Figure 10 reconstruction error matrix.
Embodiment
The present invention will be further explained below in conjunction with specific embodiment.
As shown in Figure 1, the flow process for the parasite egg recognition methods based on rarefaction representation of the present invention may further comprise the steps:
(1) set up initial dictionary: initial single category dictionary is set up in single class identification, and initial associating dictionary is set up in multiclass identification.The first step: select the less and representative parasite egg image pattern cn of some impurity, wherein c 〉=1 represents the class number, and n represents the sample number of each class, as shown in Figure 2.Second step: adopt gaussian pyramid that cn width of cloth image is compressed, obtain the image pattern behind the dimensionality reduction.The 3rd step: be spacing with the d degree, every width of cloth image that previous step is obtained rotates a circle and obtains 360/d image pattern (comprising former figure), so always sample number is N=360cn/d, can get d=5.The 4th step: each two-dimensional image data " elongation " that previous step is obtained is one-dimensional vector, again each vector is carried out standardization, makes each vector satisfy l
2-norm is 1.The 5th step: all standardized vectors that previous step is obtained obtain initial dictionary as the atom of dictionary, if c=1, what then obtain is initial single category dictionary, as shown in Figure 3, if c〉1, what then obtain is the initial associating dictionary of multiclass, comprises c sub-dictionary, as shown in Figure 4.
(2) dictionary study: use the K-SVD algorithm that dictionary is learnt, single class identification obtains single class and represents dictionary, and multiclass identification obtains uniting the expression dictionary and unites the differentiation dictionary, specifically is divided into three kinds of situations.First kind: at single class parasite egg identification, with the K-SVD algorithm initial single category dictionary is learnt, obtained single class and represent dictionary, as shown in Figure 5, this dictionary is used for Primary Location and classification simultaneously, and the volume of dictionary is decided according to the dimension of former subvector.Second kind: at the identification of multiclass parasite egg, with the K-SVD algorithm whole initial associating dictionary is learnt, obtained associating expression dictionary, as shown in Figure 6, this dictionary is used for Primary Location.The third: identify at the multiclass parasite egg, with the K-SVD algorithm each initial sub-dictionary is learnt, the more sub-dictionary after all study is united and obtained associating differentiation dictionary, as shown in Figure 7, this dictionary is used for classification, and its volume is much larger than uniting the volume of representing dictionary.
(3) handle input picture: input picture is carried out pyramidal compression, with the mode of moving window compressed image is carried out piecemeal, step-length can be chosen as one or more pixels; The all images piece is carried out rarefaction representation, and the dictionary of single class identification adopts single class to represent dictionary, and the dictionary of multiclass identification adopts associating expression dictionary.
In step (3), all images piece is carried out rarefaction representation relate to extensive rarefaction representation, use Batch-OMP algorithm solution formula (1-1).
min||x-Dθ||
2s.t.||θ||
0≤T （1-1）
Wherein x is input signal, and D is the expression dictionary that obtains in the step (3) or unites the expression dictionary, and θ is coefficient, and T is sparse property condition.
(4) calculate the reconstruction error matrix.The first step: utilize formula (1-2) to calculate reconstruction error, obtain the reconstruction error [e of all images piece
1, e
2... e
L], shown in Fig. 8 (input picture) and Fig. 9 (reconstruction error vector).
Second step: according to the position of image block at former figure, with [e
1, e
2... e
L] be arranged in a two-dimensional matrix according to the order of sequence, what obtain is the reconstruction error matrix, as shown in figure 10.
(5) obtain the candidate image piece: utilize the reconstruction error matrix that obtains in the step (4), seek its all local minimum, choose k the wherein minimum corresponding image block of value as candidate target, distribute as the local minimum point among Figure 10.
(6) identification candidate image piece: for single class identification situation, differentiate the candidate image piece with threshold value, identification is finished; For multiclass identification situation, the candidate image piece is carried out rarefaction representation, the differentiation dictionary is united in use, calculate sub-dictionary reconstruction error, use threshold mode that candidate target is differentiated and classification, all be judged as greater than the candidate image piece of threshold value for reconstruction error and do not contain the known class target, for the candidate image piece of reconstruction error less than threshold value, its classification belongs to the corresponding class of sub-dictionary of reconstruction error minimum, and identification is finished.
At this step operator dictionary reconstruction error of falling into a trap, calculate according to formula (1-3).
D wherein
iBe associating dictionary D=[D
1, D
2... D
c] sub-dictionary, i=1 wherein, 2 ..., c, c are the class number.
Claims (6)
1. the parasite egg recognition methods based on rarefaction representation is characterized in that, may further comprise the steps:
(1) set up initial dictionary: initial single category dictionary is set up in single class identification, and initial associating dictionary is set up in multiclass identification;
(2) dictionary study: use the K-SVD algorithm that dictionary is learnt, single class identification obtains single class and represents dictionary, and multiclass identification obtains uniting the expression dictionary and unites the differentiation dictionary;
(3) handle input picture: input picture is carried out pyramidal compression, with the mode of moving window compressed image is carried out piecemeal, step-length can be chosen as one or more pixels; The all images piece is carried out rarefaction representation, and the dictionary of single class identification adopts single class to represent dictionary, and the dictionary of multiclass identification adopts associating expression dictionary;
(4) calculate the reconstruction error matrix;
(5) obtain the candidate image piece: utilize the reconstruction error matrix that obtains in the step (4), seek its all local minimum, choose k the wherein minimum corresponding image block of value as candidate target;
(6) identification candidate image piece: for single class identification situation, differentiate the candidate image piece with threshold value, identification is finished; For multiclass identification situation, the candidate image piece is carried out rarefaction representation, use associating differentiation dictionary, calculate sub-dictionary reconstruction error, use threshold mode that the candidate image piece is differentiated and classification, identification is finished.
2. the parasite egg recognition methods based on rarefaction representation according to claim 1 is characterized in that: in the step (1), the initial dictionary step of described foundation is as follows:
(1) select the less and representative parasite egg image pattern cn of some impurity, wherein c is 〉=1 integer, represents the class number, and n represents the sample number of each class;
(2) adopt gaussian pyramid that cn width of cloth image is compressed, obtain the image pattern behind the dimensionality reduction;
(3) be spacing with the d degree, the every width of cloth image that obtains in the previous step is rotated a circle obtains 360/d image pattern (comprising former figure), so total sample number is N=360cn/d;
(4) each two-dimensional image data " elongation " that previous step is obtained is one-dimensional vector, again each vector is carried out standardization, makes each vector satisfy l
2-norm is 1;
(5) all standardized vectors that previous step is obtained obtain initial dictionary as the atom of dictionary, if c=1, what then obtain is initial single category dictionary of single class identification, if c＞1, what then obtain is the initial associating dictionary of multiclass identification, comprises c sub-dictionary.
3. the parasite egg recognition methods based on rarefaction representation according to claim 1 is characterized in that: in the step (2), described use K-SVD algorithm is learnt dictionary, is divided into three kinds of situations:
(1) at single class parasite egg identification, with the K-SVD algorithm initial single category dictionary is learnt, obtained single class and represent dictionary, this dictionary is used for Primary Location and classification simultaneously, and the volume of dictionary is decided according to the dimension of former subvector;
(2) at the identification of multiclass parasite egg, with the K-SVD algorithm whole initial associating dictionary is learnt, obtained associating expression dictionary, this dictionary is used for Primary Location;
(3) at multiclass parasite egg identification, with the K-SVD algorithm each initial sub-dictionary is learnt, the sub-dictionary after all study is united obtain associating differentiation dictionary again, this dictionary is used for classifying, and its volume is much larger than uniting the volume of representing dictionary.
4. the parasite egg recognition methods based on rarefaction representation according to claim 1 is characterized in that: in the step (3), described all images piece is carried out rarefaction representation is extensive rarefaction representation, namely uses Batch-OMP algorithm solution formula (1-1)
min||x-Dθ||
2s.t.||θ||
0≤T （1-1）
Wherein x is input signal, and D is that the single class that obtains in the step (2) is represented dictionary or united the expression dictionary, and θ is coefficient, and T is sparse property condition.
5. the parasite egg recognition methods based on rarefaction representation according to claim 1 is characterized in that: in the step (4), and described calculating reconstruction error matrix, step is as follows:
(1) utilizes formula (1-2) to calculate reconstruction error, obtain the reconstruction error [e of all images piece
1, e
2... e
L], wherein L is the image block number;
(2) order of the image block that obtains according to step (3) is with [e
1, e
2... e
L] be arranged in a two-dimensional matrix according to the order of sequence, what obtain is the reconstruction error matrix.
6. the parasite egg recognition methods based on rarefaction representation according to claim 1 is characterized in that: in the step (6), the sub-dictionary reconstruction error of described calculating calculates according to formula (1-3)
D wherein
iBe associating dictionary D=[D
1, D
2... D
c] sub-dictionary, i=1 wherein, 2 ..., c, c are the class number.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105138826A (en) * | 2015-08-10 | 2015-12-09 | 厦门大学 | Raman signal reconstruction method under strong noise background |
| US9933425B2 (en) | 2014-04-10 | 2018-04-03 | MEP Equine Solutions LLC | Method for the quantification of parasite eggs in feces |
| CN110119776A (en) * | 2019-05-10 | 2019-08-13 | 长沙理工大学 | Recognition methods and its system based on Multiple Kernel Learning K-SVD |
| CN111582276A (en) * | 2020-05-29 | 2020-08-25 | 北京语言大学 | Parasite egg identification method and system based on multi-feature fusion |
| TWI703513B (en) * | 2019-01-31 | 2020-09-01 | 國立成功大學 | Egg counting device and method thereof |
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| CN102891999A (en) * | 2012-09-26 | 2013-01-23 | 南昌大学 | Combined image compression/encryption method based on compressed sensing |
| CN102968635A (en) * | 2012-11-23 | 2013-03-13 | 清华大学 | Image visual characteristic extraction method based on sparse coding |
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| US20080170623A1 (en) * | 2005-04-04 | 2008-07-17 | Technion Resaerch And Development Foundation Ltd. | System and Method For Designing of Dictionaries For Sparse Representation |
| CN102073872A (en) * | 2011-01-20 | 2011-05-25 | 中国疾病预防控制中心寄生虫病预防控制所 | Image-based method for identifying shape of parasite egg |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9933425B2 (en) | 2014-04-10 | 2018-04-03 | MEP Equine Solutions LLC | Method for the quantification of parasite eggs in feces |
| US10094829B2 (en) | 2014-04-10 | 2018-10-09 | MEP Equine Solutions LLC | Method for the quantification of parasite eggs in feces |
| US10677796B2 (en) | 2014-04-10 | 2020-06-09 | MEP Equine Solutions LLC | Method for the quantification of parasite eggs in feces |
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| TWI703513B (en) * | 2019-01-31 | 2020-09-01 | 國立成功大學 | Egg counting device and method thereof |
| CN110119776A (en) * | 2019-05-10 | 2019-08-13 | 长沙理工大学 | Recognition methods and its system based on Multiple Kernel Learning K-SVD |
| CN111582276A (en) * | 2020-05-29 | 2020-08-25 | 北京语言大学 | Parasite egg identification method and system based on multi-feature fusion |
| CN111582276B (en) * | 2020-05-29 | 2023-09-29 | 北京语言大学 | Recognition method and system for parasite eggs based on multi-feature fusion |
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