Discriminative dictionary pair learning based on differentiable support vector function for visual recognition
Discriminative dictionary pair learning based on differentiable support vector function for visual recognition
复制标题
基于可微支持向量函数的视觉识别判别字典对学习
DOI：
10.1016/j.neucom.2017.07.003
复制
发表时间：
2018-01-10
期刊：
影响因子：
6
通讯作者：
Wei, Gang
中科院分区：
文献类型：
--
作者：
Chen, Boheng;Li, Jie;Wei, Gang
稀疏表示和判别字典学习（DDL）算法已成为视觉识别系统中广泛使用的一种模型，在DDL模型中引入各种判别词来提高判别能力和识别率。近年来，为了提高识别性能，提出了一种词典对学习（DPL）算法，该算法通过对一个合成字典和一个分析字典进行联合学习。本文提出了一种新的字典学习模型，在原DPL模型中引入可微支持向量判别项。在字典学习阶段，该模型可以联合训练合成字典、分析字典和支持向量判别项。在分类阶段，类标号由重建残差、投影判别项和支持向量函数的共同作用决定。在人脸识别、场景分类和目标分类等各种图像识别基准上的实验结果证明了该方法的有效性。(C) 2017 Elsevier B.V.版权所有
Sparse representation and discriminative dictionary learning (DDL) algorithm has become a widely-used model in visual recognition systems, and various discrimination terms are introduced into the DDL models to enhance the discriminative ability and the recognition rate. Recently, an algorithm named dictionary pair learning (DPL) was proposed which jointly learned a synthesis dictionary and an analysis dictionary to promote the recognition performance. In this paper, a novel dictionary learning model is proposed which introduces a differentiable support vector discriminative term into the original DPL model. In the dictionary learning stage, the proposed model can jointly train a synthesis dictionary, an analysis dictionary and a support vector discriminative term. In the classification stage, the class label is decided by the joint effect of the reconstruction residual, the projective discrimination term and the support vector function. Experimental results on various image recognition benchmarks such as face recognition, scene categorization and object classification are presented to demonstrate the effectiveness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.