A fuzzy video content representation for video summarization and content-based retrieval
Signal Processing - Special issue on fuzzy logic in signal processing
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
A new motion histogram to index motion content in video segments
Pattern Recognition Letters
Framework for measurement of the intensity of motion activity of video segments
Journal of Visual Communication and Image Representation
On clustering and retrieval of video shots through temporal slices analysis
IEEE Transactions on Multimedia
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Content analysis of video using principal components
IEEE Transactions on Circuits and Systems for Video Technology
A rule-based video annotation system
IEEE Transactions on Circuits and Systems for Video Technology
Optimal content-based video decomposition for interactive video navigation
IEEE Transactions on Circuits and Systems for Video Technology
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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.