AN EXTENDED PROBABILISTIC COLLABORATIVE REPRESENTATION BASED CLASSIFIER FOR IMAGE CLASSIFICATION

被引：0

作者：

Lan, Rushi ^{[1
,2
]}

Zhou, Yicong ^{[2
]}

机构：

[1] Guilin Univ Elect Technol, Key Lab Intelligent Proc Comp Image & Graph, Guilin, Peoples R China

[2] Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) | 2017年

关键词：

Image classification; collaborative representation based classifier (CRC); prior information; FACE RECOGNITION;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Collaborative representation based classifier (CRC) and its probabilistic improvement ProCRC have achieved satisfactory performance in many image classification applications. They, however, do not comprehensively take account of the structure characteristics of the training samples. In this paper, we present an extended probabilistic collaborative representation based classifier (EProCRC) for image classification. Compared with CRC and ProCRC, the proposed EProCRC further considers a prior information that describes the distribution of each class in the training data. This prior information enlarges the margin between different classes to enhance the discriminative capacity of EProCRC. Experiments on two challenging databases, namely CUB200-2011 and Caltech256, are conducted to evaluate EProCRC, and comparison results demonstrate that it outperforms several state-of-the-art classifiers.

引用

页码：1392 / 1397

页数：6