Paper Info
Title
Content-based image retrieval using fuzzy perceptual feedback
Abstract
In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user's fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user's fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user's feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2007
10.1007/s11042-006-0050-2
Multimedia Tools Appl.
Keywords
Field
DocType
Content-based image retrieval,Fuzzy decision,Relevance feedback,User information need
Data mining,Relevance feedback,Radial basis function,Computer science,Image retrieval,Artificial intelligence,Binary number,Computer vision,Information needs,Fuzzy logic,Perception,Machine learning,Content-based image retrieval
Journal
Volume
Issue
ISSN
32
3
1380-7501
Citations 
PageRank 
References 
2
0.38
18
Authors
2
Name
Order
Citations
PageRank
Kui Wu11336.85
Kim-Hui Yap252652.46