Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344050
Xi-Huan Yang, H. Xue, Songcan Chen
Local Ridge Regression Classifier (LRR) is an effective local face recognition method. It suppresses the influence of local changes by setting a voting RR classifier for each image region, thus has partial robustness to local changes caused by lighting, occlusions and poses. LRR uses the concatenated vector of a sub-image as its input feature, such a feature is still not sufficient to represent an image, thus leading to possibly imprecise voting and limited increase in recognition rate. In order to boost its recognition rate, we first develop a novel classifier GLRR which combines LRR classifier and Gabor-LBP features which can improve the feature representation greatly. Experiments on AR database demonstrate that GLRR is superior to LRR and other local methods such as Aw-SpPCA and SpCCA. When just fewer classifiers can be available and some occlusion regions exist, majority-voting recognition rate will still be imprecise. To remedy this, in this paper, we add an occlusion detection step before classification using GLRR for which we call it S-GLRR. In this way, we can purposely shield locally-occluded regions using the detection step, thus get better performance for face recognition. Experiments show that S-GLRR achieves better recognition rate than GLRR, especially when only a few sub-classifiers are provided.
{"title":"Image Region Selection Based GLRR for Face Recognition","authors":"Xi-Huan Yang, H. Xue, Songcan Chen","doi":"10.1109/CCPR.2009.5344050","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344050","url":null,"abstract":"Local Ridge Regression Classifier (LRR) is an effective local face recognition method. It suppresses the influence of local changes by setting a voting RR classifier for each image region, thus has partial robustness to local changes caused by lighting, occlusions and poses. LRR uses the concatenated vector of a sub-image as its input feature, such a feature is still not sufficient to represent an image, thus leading to possibly imprecise voting and limited increase in recognition rate. In order to boost its recognition rate, we first develop a novel classifier GLRR which combines LRR classifier and Gabor-LBP features which can improve the feature representation greatly. Experiments on AR database demonstrate that GLRR is superior to LRR and other local methods such as Aw-SpPCA and SpCCA. When just fewer classifiers can be available and some occlusion regions exist, majority-voting recognition rate will still be imprecise. To remedy this, in this paper, we add an occlusion detection step before classification using GLRR for which we call it S-GLRR. In this way, we can purposely shield locally-occluded regions using the detection step, thus get better performance for face recognition. Experiments show that S-GLRR achieves better recognition rate than GLRR, especially when only a few sub-classifiers are provided.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128807261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344086
N.Muru gan, F. Zheng
In the complex stomach epidermis tumor cells, the traditional segmentation algorithms such as the K-means clustering algorithm and the simple threshold segmentation algorithm are unable to get satisfactory results. The relaxation iterative segmentation algorithm can segment the cell clearly, but it wastes a lot of time and the execution efficiency is very low. In this paper the authors propose a new segmentation algorithm based on the maximization of Mutual information in effective information, in which to find the optimal threshold values to segment the stomach epidermis tumor cells.
{"title":"Stomach Epidermis Tumor Cell Segmentation Based on the Maximization of Mutual Information in Effective Information","authors":"N.Muru gan, F. Zheng","doi":"10.1109/CCPR.2009.5344086","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344086","url":null,"abstract":"In the complex stomach epidermis tumor cells, the traditional segmentation algorithms such as the K-means clustering algorithm and the simple threshold segmentation algorithm are unable to get satisfactory results. The relaxation iterative segmentation algorithm can segment the cell clearly, but it wastes a lot of time and the execution efficiency is very low. In this paper the authors propose a new segmentation algorithm based on the maximization of Mutual information in effective information, in which to find the optimal threshold values to segment the stomach epidermis tumor cells.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123259324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5343963
Fengxi Song, Yong Xu, David Zhang, Tianwei Liu
This paper presented a novel subspace-based facial discriminant feature extraction method, i.e. Orthogonalized Direct Linear Discriminant Analysis (OD-LDA), whose discriminant vectors could be obtained by performing Gram-Schmidt orthogonal procedure on a set of discriminant vectors of D-LDA. Experimental studies conducted on ORL, FERET, Yale, and AR face image databases showed that OD-LDA could compete with prevailing subspace-based facial discriminant feature extraction methods such as Fisherfaces, N-LDA D-LDA, Uncorrelated LDA, Parameterized D-LDA, K-L expansion based the between-class scatter matrix, and Orthogonal Complimentary Space Method in terms of recognition rate.
{"title":"A Novel Subspace-Based Facial Discriminant Feature Extraction Method","authors":"Fengxi Song, Yong Xu, David Zhang, Tianwei Liu","doi":"10.1109/CCPR.2009.5343963","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5343963","url":null,"abstract":"This paper presented a novel subspace-based facial discriminant feature extraction method, i.e. Orthogonalized Direct Linear Discriminant Analysis (OD-LDA), whose discriminant vectors could be obtained by performing Gram-Schmidt orthogonal procedure on a set of discriminant vectors of D-LDA. Experimental studies conducted on ORL, FERET, Yale, and AR face image databases showed that OD-LDA could compete with prevailing subspace-based facial discriminant feature extraction methods such as Fisherfaces, N-LDA D-LDA, Uncorrelated LDA, Parameterized D-LDA, K-L expansion based the between-class scatter matrix, and Orthogonal Complimentary Space Method in terms of recognition rate.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116134891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344093
Shijin Li, Jiali Zhu, Xiangtao Gao, Jian Tao
Soil erosion is one of the most typical natural disasters in China. However, due to the limitation of current technology, the investigation of soil erosion through remote sensing images is currently by human beings manually which depends on human interpretation and interactive selection. The work burden is so heavy that errors are usually inevitably unavoidable. This paper proposes the technique of content-based image retrieval to tackle this problem. Due to the large amount of computation in co-training retrieval based on multiple classifier systems, and for the purpose of improving efficiency, an improved approach using co-training in two classifier systems is proposed in this paper. Prior to retrieving, we firstly select the optimal color feature and texture feature respectively, and then use the corresponding color classifier and texture classifier for co-training. By this approach, the time of co-training is reduced greatly, meanwhile, the selected optimal features can represent color and texture features better for remote sensing image, resulting in better retrieval accuracy. Experimental results show that the improved approach using co-training in two classifier systems needs less amount of computation and less retrieval time, while it can lead to better retrieval results.
{"title":"Soil Erosion Remote Sensing Image Retrieval Based on Semi-Supervised Learning","authors":"Shijin Li, Jiali Zhu, Xiangtao Gao, Jian Tao","doi":"10.1109/CCPR.2009.5344093","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344093","url":null,"abstract":"Soil erosion is one of the most typical natural disasters in China. However, due to the limitation of current technology, the investigation of soil erosion through remote sensing images is currently by human beings manually which depends on human interpretation and interactive selection. The work burden is so heavy that errors are usually inevitably unavoidable. This paper proposes the technique of content-based image retrieval to tackle this problem. Due to the large amount of computation in co-training retrieval based on multiple classifier systems, and for the purpose of improving efficiency, an improved approach using co-training in two classifier systems is proposed in this paper. Prior to retrieving, we firstly select the optimal color feature and texture feature respectively, and then use the corresponding color classifier and texture classifier for co-training. By this approach, the time of co-training is reduced greatly, meanwhile, the selected optimal features can represent color and texture features better for remote sensing image, resulting in better retrieval accuracy. Experimental results show that the improved approach using co-training in two classifier systems needs less amount of computation and less retrieval time, while it can lead to better retrieval results.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122901312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344054
Chuan-Xian Ren, D. Dai
Sparse representation for robust face recognition is a novel concept in the pattern analysis and machine learning community. Through the l1-minimization model, representing a test sample as the sparse combination of the training dictionary can effectively achieve facial images classification. However, when the number of training samples is relatively small, it is insufficient to give the test sample a sparse representation so that the recognition performance degenerates seriously. In this paper, we present a novel approach that employs the Elastic Net regularized regression model. Experimental results on several databases show that the proposed strategy improves the recognition accuracy.
{"title":"Sparse Representation by Adding Noisy Duplicates for Enhanced Face Recognition: An Elastic Net Regularization Approach","authors":"Chuan-Xian Ren, D. Dai","doi":"10.1109/CCPR.2009.5344054","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344054","url":null,"abstract":"Sparse representation for robust face recognition is a novel concept in the pattern analysis and machine learning community. Through the l1-minimization model, representing a test sample as the sparse combination of the training dictionary can effectively achieve facial images classification. However, when the number of training samples is relatively small, it is insufficient to give the test sample a sparse representation so that the recognition performance degenerates seriously. In this paper, we present a novel approach that employs the Elastic Net regularized regression model. Experimental results on several databases show that the proposed strategy improves the recognition accuracy.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131115475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344020
Yongming Huang, Guobao Zhang, Xiaoli Xu
In this paper, we present an emotion recognition system using the stacked generalization ensemble neural network for special human affective state in the speech signal. 450 short emotional sentences with different contents from 3 speakers were collected as experiment materials. The features relevant with energy, speech rate, pitch and formant are extracted from speech signals. Stacked Generalization Ensemble Neural Networks are used as the classifier for 5 emotions including anger, calmness, happiness, sadness and boredom. First, compared with the traditional BP network or wavelet neural network, the results of experiments show that the Stacked Generalization Ensemble Neural Network has faster convergence speed and higher recognition rate. Second, after discussing the advantage and disadvantage between different ensemble Neural Networks, suitable decision will be made for Robot Pet.
{"title":"Speech Emotion Recognition Research Based on the Stacked Generalization Ensemble Neural Network for Robot Pet","authors":"Yongming Huang, Guobao Zhang, Xiaoli Xu","doi":"10.1109/CCPR.2009.5344020","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344020","url":null,"abstract":"In this paper, we present an emotion recognition system using the stacked generalization ensemble neural network for special human affective state in the speech signal. 450 short emotional sentences with different contents from 3 speakers were collected as experiment materials. The features relevant with energy, speech rate, pitch and formant are extracted from speech signals. Stacked Generalization Ensemble Neural Networks are used as the classifier for 5 emotions including anger, calmness, happiness, sadness and boredom. First, compared with the traditional BP network or wavelet neural network, the results of experiments show that the Stacked Generalization Ensemble Neural Network has faster convergence speed and higher recognition rate. Second, after discussing the advantage and disadvantage between different ensemble Neural Networks, suitable decision will be made for Robot Pet.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115167713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344146
Qianru Li, H. Wang, J. Yang
Collaborative filtering technology has been successfully used in personalized recommendation systems. With the development of E-commerce, as well as the increase in the number of users and items, the users score data sparsity and the dimension disaster problems have been caused which leads to sharp decline in the quality of their recommend. A calculation of pattern similarity was proposed based on the users pattern similarity to direct at the sparsity and dimension disadvantage of high-dimensional data. Clustering were produced by subspace clustering algorithm based on users pattern similarity, and collaborative filtering algorithm was improved by calculating of model similarity which brings recommendation to users. The experimental result shows that algorithm increase the response speed of the system,at the mean time the recommendation quality has been improved a lot.
{"title":"Collaborative Filtering in Personalized Recommendation Based on Users Pattern Subspace Clustering","authors":"Qianru Li, H. Wang, J. Yang","doi":"10.1109/CCPR.2009.5344146","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344146","url":null,"abstract":"Collaborative filtering technology has been successfully used in personalized recommendation systems. With the development of E-commerce, as well as the increase in the number of users and items, the users score data sparsity and the dimension disaster problems have been caused which leads to sharp decline in the quality of their recommend. A calculation of pattern similarity was proposed based on the users pattern similarity to direct at the sparsity and dimension disadvantage of high-dimensional data. Clustering were produced by subspace clustering algorithm based on users pattern similarity, and collaborative filtering algorithm was improved by calculating of model similarity which brings recommendation to users. The experimental result shows that algorithm increase the response speed of the system,at the mean time the recommendation quality has been improved a lot.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344155
Jie-sheng Wang, Xian-wen Gao
Aiming at the predifined clustering number, strong randomness and easiness to fall into local optimum , a new self-adaptive FCM algorithm based on genetic algorithm is proposed. The number of fuzzy clustering and cluster centers are optimized by sizable-chromosome genetic algorithms (SC-GAs). Cut operator and splice operator are adopted to combination the chromosome to form new individuals. Non-uniform mutation operator is used to enhance the population diversity. The new proposed method can obtain the global optimam compared to standard FCM algorithm. The simulation experimental result s with IRIS demonstrate the feasibility and effectiveness of the new algorithm.
{"title":"Optimization of Fuzzy C-Means Clustering by Genetic Algorithms Based on Sizable Chromosome","authors":"Jie-sheng Wang, Xian-wen Gao","doi":"10.1109/CCPR.2009.5344155","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344155","url":null,"abstract":"Aiming at the predifined clustering number, strong randomness and easiness to fall into local optimum , a new self-adaptive FCM algorithm based on genetic algorithm is proposed. The number of fuzzy clustering and cluster centers are optimized by sizable-chromosome genetic algorithms (SC-GAs). Cut operator and splice operator are adopted to combination the chromosome to form new individuals. Non-uniform mutation operator is used to enhance the population diversity. The new proposed method can obtain the global optimam compared to standard FCM algorithm. The simulation experimental result s with IRIS demonstrate the feasibility and effectiveness of the new algorithm.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132397530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344101
Jian-hua Yuan
The super-resolution image reconstruction is an ill-posed problem, which need regularizing during the reconstruction. The super-resolution image was modeled a two-dimensional manifold embedded in a three-dimensional space. The regularization constraint in the reconstruction was that the image was the minimal surface on the two-dimensional manifold. The algorithm broadened the image restoration algorithms based on the partial differential equation, and the TV restoration algorithm was a particular case of the minimal surface constraint reconstruction algorithm. The experiments show the algorithm could reconstruct the super-resolution image efficiently.
{"title":"Super-Resolution Image Reconstruction Based on the Minimal Surface Constraint on the Manifold","authors":"Jian-hua Yuan","doi":"10.1109/CCPR.2009.5344101","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344101","url":null,"abstract":"The super-resolution image reconstruction is an ill-posed problem, which need regularizing during the reconstruction. The super-resolution image was modeled a two-dimensional manifold embedded in a three-dimensional space. The regularization constraint in the reconstruction was that the image was the minimal surface on the two-dimensional manifold. The algorithm broadened the image restoration algorithms based on the partial differential equation, and the TV restoration algorithm was a particular case of the minimal surface constraint reconstruction algorithm. The experiments show the algorithm could reconstruct the super-resolution image efficiently.","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134278236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-12-04DOI: 10.1109/CCPR.2009.5344128
Zhenghong Gu, Jian Yang
Marginal information is of great importance for classification. This paper presents a new nonparametric linear discriminant analysis method named Push-Pull marginal discriminant analysis (PPMDA) which takes full advantage of marginal information. For two-class cases, the idea of this method is to determine projection directions such that the marginal samples of one class are pushed away from the between-class marginal samples as far as possible and simultaneously pulled to the within-class samples as close as possible. This idea can be extended for multi-class cases and gives rise to the PPMDA algorithm for feature extraction of multi-class problems. The proposed method is evaluated using the Extended Yale face database B and the ORL database. Experimental results show the effectiveness of the proposed method and its performance advantage over the state-of-art feature extraction methods
{"title":"A New Nonparametric Linear Discriminant Analysis Method Based on Marginal Information","authors":"Zhenghong Gu, Jian Yang","doi":"10.1109/CCPR.2009.5344128","DOIUrl":"https://doi.org/10.1109/CCPR.2009.5344128","url":null,"abstract":"Marginal information is of great importance for classification. This paper presents a new nonparametric linear discriminant analysis method named Push-Pull marginal discriminant analysis (PPMDA) which takes full advantage of marginal information. For two-class cases, the idea of this method is to determine projection directions such that the marginal samples of one class are pushed away from the between-class marginal samples as far as possible and simultaneously pulled to the within-class samples as close as possible. This idea can be extended for multi-class cases and gives rise to the PPMDA algorithm for feature extraction of multi-class problems. The proposed method is evaluated using the Extended Yale face database B and the ORL database. Experimental results show the effectiveness of the proposed method and its performance advantage over the state-of-art feature extraction methods","PeriodicalId":354468,"journal":{"name":"2009 Chinese Conference on Pattern Recognition","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133962556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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