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A Query Expansion Framework in Image Retrieval Domain Based on Local and Global Analysis.

M M Rahman1, S K Antani, G R Thoma

  • 1U.S. National Library of Medicine, National Institutes of Health, Bethesda, MB, USA.

Information Processing & Management
|August 9, 2011
PubMed
Summary
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This study introduces an image retrieval system using concept features and automatic query expansion. The framework enhances search precision and recall by analyzing concept correlations.

Area of Science:

  • Computer Science
  • Information Retrieval
  • Image Analysis

Background:

  • Traditional text retrieval uses keyword vectors.
  • Image retrieval often lacks robust concept representation and query expansion.

Purpose of the Study:

  • To develop an image retrieval framework using automatic query expansion in a concept feature space.
  • To generalize the vector space model for image retrieval.

Main Methods:

  • Images represented as vectors of weighted concepts ('bag of concepts') derived from color and texture patches.
  • Support Vector Machine (SVM) classification used for concept vocabulary generation.
  • Query expansion techniques based on local (co-occurrence, neighborhood proximity) and global (similarity thesaurus) concept analysis.

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Main Results:

  • Demonstrated effectiveness on natural scenes and biomedical image databases.
  • Improved precision and recall in image retrieval tasks.
  • Addressed feature independence assumption via concept correlation analysis.

Conclusions:

  • The proposed framework offers an effective approach to image retrieval.
  • Automatic query expansion in concept feature space significantly enhances retrieval performance.
  • The method is applicable across diverse image collections.