Mining high-level user concepts with multiple instance learning and relevance feedback for content-based image retrieval

Xin Huang, Shu Ching Chen, Mei Ling Shyu, Chengcui Zhang

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this book chapter, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role the user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested to show the feasibility and effectiveness of the proposed multimedia data mining framework.

Original languageEnglish (US)
Pages (from-to)50-67
Number of pages18
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2797
StatePublished - Dec 1 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Mining high-level user concepts with multiple instance learning and relevance feedback for content-based image retrieval'. Together they form a unique fingerprint.

  • Cite this