Integrating absolute distances in collaborative representation for robust image classification  

作　　者：Shaoning Zeng Xiong Yang Jianping Gou Jiajun Wen Shaoning Zeng;Xiong Yang;Jianping Gou;Jiajun Wen(Department of Computer Science, Huizhou University, 46 Yanda Road, Huizhou, Guangdong, China;College of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, China;Institute of Textiles and Clothing, Hong Kong Polytechnic University, Room QTT15, Q Core, 7/F, Hong Kong)

机构地区：[1]Department of Computer Science, Huizhou University, 46 Yanda Road, Huizhou, Guangdong, China [2]College of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu, China [3]Institute of Textiles and Clothing, Hong Kong Polytechnic University, Room QTT15, Q Core, 7/F, Hong Kong

出　　处：《CAAI Transactions on Intelligence Technology》2016年第2期189-196,共8页智能技术学报（英文）

摘　　要：Conventional sparse representation based classification （SRC） represents a test sample with the coefficient solved by each training sample in all classes. As a special version and improvement to SRC, collaborative representation based classification （CRC） obtains representation with the contribution from all training samples and produces more promising results on facial image classification. In the solutions of representation coefficients, CRC considers original value of contributions from all samples. However, one prevalent practice in such kind of distance-based methods is to consider only absolute value of the distance rather than both positive and negative values. In this paper, we propose an novel method to improve collaborative representation based classification, which integrates an absolute distance vector into the residuals solved by collaborative representation. And we named it AbsCRC. The key step in AbsCRC method is to use factors a and b as weight to combine CRC residuals rescrc with absolute distance vector disabs and generate a new dviaetion r = a·rescrc b.disabs, which is in turn used to perform classification. Because the two residuals have opposite effect in classification, the method uses a subtraction operation to perform fusion. We conducted extensive experiments to evaluate our method for image classification with different instantiations. The experimental results indicated that it produced a more promising result of classification on both facial and non-facial images than original CRC method.

关 键 词：Sparse representation Collaborative representation INTEGRATION Image classification Face recognition 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术] TP18