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General regression and representation model for classification.

Qian J, Yang J, Xu Y - PLoS ONE (2014)

Bottom Line: In real-world applications, this assumption does not hold.Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample.The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

ABSTRACT
Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients) and the specific information (weight matrix of image pixels) to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR) and robust general regression and representation classifier (R-GRR). The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

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Related in: MedlinePlus

The recognition rates of each classifier for face recognition on AR database with disguise occlusion.(a) The testing images with sunglasses from session 1; (b) The testing images with scarves from session 1; (c) The testing images with sunglasses from session 2; (d) The testing images with scarves from session 2.
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pone-0115214-g010: The recognition rates of each classifier for face recognition on AR database with disguise occlusion.(a) The testing images with sunglasses from session 1; (b) The testing images with scarves from session 1; (c) The testing images with sunglasses from session 2; (d) The testing images with scarves from session 2.

Mentions: In the second experiment, four neutral images with different illumination from the first session of each individual are used for training. The disguise images with various illumination and glasses or scarves per individual in session 1 and session 2 for testing. We set the parameter K as 220, 300, 240 and 320 for the four different test sets, respectively. The recognition rates of each method are shown in Fig. 10. From Fig. 10, we can see clearly that R-GRR gives better performance than CRC, SRC, GSRC, CESR, RSC and RRC_L2 on different testing subsets. Both SRC and CESR do well on the subsets with sunglasses but poor in the cases with scarves. However, GSRC achieves better result on the subsets with scarves and worse result on the subsets with sunglasses. Compared to RSC, at least 4.3% improvement is achieved by R-GRR for different testing set. Meanwhile, it is worth noticing that the recognition rate of R-GRR is 67.6%, 59.6% higher than SRC and CESR on the testing images with scarves from session 2, and 43.7% higher than GSRC on the testing images with sunglasses from session 2. In the first two subsets from session 1, the performances of R-GRR and RRC_L2 are similar. However, R-GRR significantly outperforms RRC_L2 in the last two subsets (more challenge tasks) from session 2. Compared with RRC_L2, R-GRR uses instead of to refine the regularization term can further improve the classification performance.


General regression and representation model for classification.

Qian J, Yang J, Xu Y - PLoS ONE (2014)

The recognition rates of each classifier for face recognition on AR database with disguise occlusion.(a) The testing images with sunglasses from session 1; (b) The testing images with scarves from session 1; (c) The testing images with sunglasses from session 2; (d) The testing images with scarves from session 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4274033&req=5

pone-0115214-g010: The recognition rates of each classifier for face recognition on AR database with disguise occlusion.(a) The testing images with sunglasses from session 1; (b) The testing images with scarves from session 1; (c) The testing images with sunglasses from session 2; (d) The testing images with scarves from session 2.
Mentions: In the second experiment, four neutral images with different illumination from the first session of each individual are used for training. The disguise images with various illumination and glasses or scarves per individual in session 1 and session 2 for testing. We set the parameter K as 220, 300, 240 and 320 for the four different test sets, respectively. The recognition rates of each method are shown in Fig. 10. From Fig. 10, we can see clearly that R-GRR gives better performance than CRC, SRC, GSRC, CESR, RSC and RRC_L2 on different testing subsets. Both SRC and CESR do well on the subsets with sunglasses but poor in the cases with scarves. However, GSRC achieves better result on the subsets with scarves and worse result on the subsets with sunglasses. Compared to RSC, at least 4.3% improvement is achieved by R-GRR for different testing set. Meanwhile, it is worth noticing that the recognition rate of R-GRR is 67.6%, 59.6% higher than SRC and CESR on the testing images with scarves from session 2, and 43.7% higher than GSRC on the testing images with sunglasses from session 2. In the first two subsets from session 1, the performances of R-GRR and RRC_L2 are similar. However, R-GRR significantly outperforms RRC_L2 in the last two subsets (more challenge tasks) from session 2. Compared with RRC_L2, R-GRR uses instead of to refine the regularization term can further improve the classification performance.

Bottom Line: In real-world applications, this assumption does not hold.Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample.The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

ABSTRACT
Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients) and the specific information (weight matrix of image pixels) to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR) and robust general regression and representation classifier (R-GRR). The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

Show MeSH
Related in: MedlinePlus