A novel fusion strategy for probabilistic sparse representation classifier guided by support vector machines

Jianhang Zhou; Shaoning Zeng; Bob Zhang

doi:10.1117/12.2539642

14 August 2019 A novel fusion strategy for probabilistic sparse representation classifier guided by support vector machines

Jianhang Zhou, Shaoning Zeng, Bob Zhang

Author Affiliations +

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117935 (2019) https://doi.org/10.1117/12.2539642
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

In recent object recognition research, the Sparse Representation based Classifier (SRC) and Collaborative Representation based Classification (CRC) have been widely used, achieving promising performances and robustness. However, both of these two algorithms are seldomly fused in classification based on the theory of probability. In this paper, we propose a novel image classification algorithm named Probabilistic Sparse-Collaborative Representation based Classifier (PSCRC), by fusing SRC and CRC. To boost the recognition performance and maintain the robustness of SRC, we introduce the theory of probability to offer different weights for each element in the coefficient vectors of SRC and CRC, respectively. We generate the probabilities of each sample in the training set by using Support Vector Machines (SVMs) which are fused with the coefficients of SRC and CRC. The proposed method is verified on five popular real word image datasets while being compared with other classifiers. The numerical results in the experiments show that the proposed classifier using our fusion strategy outperforms others.

Citation Download Citation

Jianhang Zhou, Shaoning Zeng, and Bob Zhang "A novel fusion strategy for probabilistic sparse representation classifier guided by support vector machines", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117935 (14 August 2019); https://doi.org/10.1117/12.2539642

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