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Multimedia Tools and Applications

, Volume 21, Issue 1, pp 35–54 | Cite as

Alternating Feature Spaces in Relevance Feedback

  • Fang Qian
  • Mingjing Li
  • Hong-Jiang Zhang
  • Wei-Ying Ma
  • Bo Zhang
Article
  • 49 Downloads

Abstract

Image retrieval using relevance feedback can be treated as a two-class learning and classification process. The user-labelled relevant and irrelevant images are regarded as positive and negative training samples, based on which a classifier is trained dynamically. Then the classifier in turn classifies all images in the database. In practice, the number of training samples is very small because the users are often impatient. On the other hand, the positive samples usually are not representative since they are the nearest ones to the query and thus less informative. The insufficiency of training samples both in quantities and varieties constrains the generalization ability of the classifier significantly. In this paper, we propose a novel relevance feedback approach, which aims to collect more representative samples and hence improve the performance of classifier. Image labeling and classifier training are conducted in two complementary image feature spaces. Since the samples distribute differently in two spaces, the positive samples may be more informative in one feature space than in another. The two complementary feature spaces are alternated iteratively during the feedback process. To choose appropriate complementary feature spaces, we present two methods to measure the complementarities between two feature spaces quantitatively. Our experimental result on 10,000 images indicates that the proposed feedback approach significantly improves image retrieval performance.

image retrieval relevance feedback complementary features representative training samples 

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Fang Qian
    • 1
  • Mingjing Li
    • 2
  • Hong-Jiang Zhang
    • 2
  • Wei-Ying Ma
    • 2
  • Bo Zhang
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Microsoft Research AsiaBeijingChina

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