Paper Info
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.
Year
DOI
Venue
2006
10.1007/11612704_30
ACCV
Keywords
Field
DocType
weighting importance,region weighting scheme,positive feedbacks,region-based image retrieval,proposed system,content-based image retrieval,single positive feedback group,region-based relevance feedbacks,relevance feedback,heuristic pre-clustering relevance feedback,discriminant analysis,positive feedback
Similitude,Computer vision,Heuristic,Weighting,Relevance feedback,Pattern recognition,Similarity measure,Computer science,Image processing,Image retrieval,Artificial intelligence,Cluster analysis
Conference
Volume
ISSN
ISBN
3852
0302-9743
3-540-31244-7
Citations 
PageRank 
References 
2
0.38
15
Authors
3
Name
Order
Citations
PageRank
Wan-Ting Su1162.69
Wen-Sheng Chu238014.54
Jenn-Jier James Lien314314.42