Heuristic pre-clustering relevance feedback in region-based image retrieval

Wan Ting Su, Wen Sheng Chu, James Jenn-Jier Lien

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Relevance feedback (RF) and region-based image retrieval (RBIR) are two widely used methods to enhance the performance of content-based image retrieval (CBIR) systems. In this paper, these two methods are combined. And a region weighting scheme reflecting the process of human visual perception is also proposed to enhance the weighting importance assigned to the region whose pixels are closer to the attention center. Furthermore, rather than using a single positive feedback group, the proposed approach introduces RBIR to the relevance feedback with multiple positive and negative groups. To guide users in grouping the positive feedbacks, the proposed system provides a heuristic pre-clustering result automatically. Using these guiding clusters, the users can re-group the positive feedbacks to express his/her particular interests. Finally, Group Biased Discriminant Analysis (GBDA) is modified and applied to the similarity measure between images constructed on the basis of the region-based relevance feedbacks.

Original languageEnglish
Pages (from-to)294-304
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3852 LNCS
DOIs
Publication statusPublished - 2006 Jun 14
Event7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India
Duration: 2006 Jan 132006 Jan 16

Fingerprint

Relevance Feedback
Image retrieval
Image Retrieval
Clustering
Positive Feedback
Heuristics
Feedback
Weighting
Visual Perception
Human Perception
Content-based Image Retrieval
Discriminant Analysis
Similarity Measure
Grouping
Discriminant analysis
Biased
Express
Pixel
Pixels

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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title = "Heuristic pre-clustering relevance feedback in region-based image retrieval",
abstract = "Relevance feedback (RF) and region-based image retrieval (RBIR) are two widely used methods to enhance the performance of content-based image retrieval (CBIR) systems. In this paper, these two methods are combined. And a region weighting scheme reflecting the process of human visual perception is also proposed to enhance the weighting importance assigned to the region whose pixels are closer to the attention center. Furthermore, rather than using a single positive feedback group, the proposed approach introduces RBIR to the relevance feedback with multiple positive and negative groups. To guide users in grouping the positive feedbacks, the proposed system provides a heuristic pre-clustering result automatically. Using these guiding clusters, the users can re-group the positive feedbacks to express his/her particular interests. Finally, Group Biased Discriminant Analysis (GBDA) is modified and applied to the similarity measure between images constructed on the basis of the region-based relevance feedbacks.",
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