Accumulated motion energy fields estimation and representation for semantic event detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
SPIRAL: efficient and exact model identification for hidden Markov models
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative optical flow tensor for video semantic analysis
Computer Vision and Image Understanding
Client-centered multimedia content adaptation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fast likelihood search for hidden Markov models
ACM Transactions on Knowledge Discovery from Data (TKDD)
Automatic sports genre categorization and view-type classification over large-scale dataset
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Semantic concept mining in cricket videos for automated highlight generation
Multimedia Tools and Applications
Vlogging: A survey of videoblogging technology on the web
ACM Computing Surveys (CSUR)
A semantic framework for video genre classification and event analysis
Image Communication
Combining multimodal and temporal contextual information for semantic video analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
Modeling television schedules for television stream structuring
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
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Scene classification and segmentation are fundamental steps for efficient accessing, retrieving and browsing large amount of video data. We have developed a scene classification scheme using a Hidden Markov Model (HMM)-based classifier. By utilizing the temporal behaviors of different scene classes, HMM classifier can effectively classify presegmented clips into one of the predefined scene classes. In this paper, we describe three approaches for joint classification and segmentation based on HMM, which search for the most likely class transition path by using the dynamic programming technique. All these approaches utilize audio and visual information simultaneously. The first two approaches search optimal scene class transition based on the likelihood values computed for short video segment belonging to a particular class but with different search constrains. The third approach searches the optimal path in a super HMM by concatenating HMM's for different scene classes.