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Particle Swarm Optimization for Automatic Selection of Relevance Feedback Heuristics

  • Peng-Yeng Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

Relevance feedback (RF) is an iterative process which refines the retrievals by utilizing user’s feedback marked on retrieved results. Recent research has focused on the optimization for RF heuristic selection. In this paper, we propose an automatic RF heuristic selection framework which automatically chooses the best RF heuristic for the given query. The proposed method performs two learning tasks: query optimization and heuristic-selection optimization. The particle swarm optimization (PSO) paradigm is applied to assist the learning tasks. Experimental results tested on a content-based retrieval system with a real-world image database reveal that the proposed method outperforms several existing RF approaches using different techniques. The convergence behavior of the proposed method is empirically analyzed.

Keywords

information retrieval relevance feedback long-term learning heuristic selection particle swarm optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Peng-Yeng Yin
    • 1
  1. 1.Department of Information ManagementNational Chi-Nan UniversityNantouTaiwan

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