MPEG-7 DESCRIPTOR SELECTION USING LOCALIZED GENERALIZATION ERROR MODEL WITH MUTUAL INFORMATION

摘要：

MPEG-7 provides a set of descriptors to describe the content of an image. However, how to select or combine descriptors for a specific image classification problem is still an open problem. Currently, descriptors are usually selected by human experts. Moreover, selecting the same set of descriptors for different classes of images may not be reasonable. In this work we propose a MPEG-7 descriptor selection method which selects different MPEG-7 descriptors for different image class in an image classification problem. The proposed method L-GEMIM combines Localized Generalization Error Model (L-GEM) and Mutual Information (MI) to assess the relevance of MPEG-7 descriptors for a particular image class. The L-GEMIM model assesses the relevance based on the generalization capability of a MPEG-7 descriptor using L-GEM and prevents redundant descriptors being selected by MI. Experimental results using 4,000 images in 4 classes show that L-GEMIM selects better set of MPEG-7 descriptors yielding a higher testing accuracy of image classification.

关键词： MPEG-7 Descriptor Selection Localized Generalization Error Model Mutual Information Image Classification

作者: JUN WANG WING W.Y.NG ERIC C.C.TSANG TAO ZHU BINBIN SUN DANIEL S.YEUNG

作者单位: Media and Life Science Computing Lab, Shenzhen Graduate School, Harbin Institute of Technology, Chin Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 454-459

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）