Paper Info
Title
FCBIR: A Fuzzy Matching Technique for Content-Based Image Retrieval
Abstract
Semantic image retrieval basically can be viewed as a pattern recognition problem. For human, pattern recognition is inherent in herself/himself by the inference rules through a long time experience. However, for computer, on the one hand, the simulated human identification of objects is impressive at its experience (training) like a baby learns to identify objects; on the other hand, the precise identification is unreasonable because the similar features are usually shared by different objects, e.g., "an white animal like cat and dog", "a structural transportation like car and truck". In traditional approaches, disambiguate the images by eliminating irrelevant semantics does not fit in with human behavior. Accordingly, the ambiguous concepts of each image estimated throughout the collaboration of similarity function and membership function is sensible. To this end, in this paper, we propose a novel fuzzy matching technique named Fuzzy Content-Based Image Retrieval (FCBIR) that primarily contains three characteristics: 1) conceptualize image automatically, 2) identify image roughly, and 3) retrieve image efficiently. Out of human perspective, experiments reveal that our proposed approach can bring out good results effectively and efficiently in terms of image retrieval.
Year
DOI
Venue
2007
10.1007/978-3-540-72434-6_15
THEORETICAL ADVANCES AND APPLICATIONS OF FUZZY LOGIC AND SOFT COMPUTING
Keywords
Field
DocType
multimedia database,content-based image retrieval,data mining,fuzzy set,fuzzy search
Computer vision,Automatic image annotation,Computer science,Fuzzy logic,Image retrieval,Fuzzy set,Approximate string matching,Artificial intelligence,Membership function,Content-based image retrieval,Visual Word
Conference
Volume
ISSN
Citations 
42.0
1615-3871
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Vincent S. Tseng12923161.33
Ja-Hwung Su232924.53
Wei-Jyun Huang3592.05