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

  • Authors:

  • Wan-Ting Su;Wen-Sheng Chu;Jenn-Jier James Lien

  • Affiliations:

  • Robotics Laboratory, Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;Robotics Laboratory, Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;Robotics Laboratory, Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

  • Venue:
  • ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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.