Paper Info
Title
Video indexing and retrieval in compressed domain using fuzzy-categorization
Abstract
There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzy-categorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: full-court match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.
Year
DOI
Venue
2006
10.1007/11919629_24
ISVC
Keywords
Field
DocType
new fuzzy feature,retrieval system,retrieval performance,content-based video retrieval system,fuzzy membership function,video indexing,semantic-based video retrieval,temporal feature extraction,indexing feature,basketball video,indexation,composition operator,feature extraction
Data mining,Computer science,Image processing,Image retrieval,Search engine indexing,Artificial intelligence,Computer vision,Pattern recognition,Fuzzy logic,Feature extraction,Decoding methods,Membership function,Visual Word
Conference
Volume
ISSN
ISBN
4292
0302-9743
3-540-48626-7
Citations 
PageRank 
References 
0
0.34
12
Authors
3
Name
Order
Citations
PageRank
Hui Fang111414.47
Rami Qahwaji212021.05
Jianmin Jiang398581.39