Relevance feature mapping for content-based multimedia information retrieval

Authors:
Guang-Tong Zhou;Kai Ming Ting;Fei Tony Liu;Yilong Yin
Affiliations:
School of Computer Science and Technology, Shandong University, Jinan 250101, China;Gippsland School of Information Technology, Monash University, Victoria 3842, Australia;Gippsland School of Information Technology, Monash University, Victoria 3842, Australia;School of Computer Science and Technology, Shandong University, Jinan 250101, China
Venue:
Pattern Recognition
Year:
2012

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Abstract

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.