Shot boundary detection in MPEG videos using local and global indicators

Shot boundary detection (SBD) plays important roles in many video applications. In this letter, we describe a novel method on SBD operating directly in the compressed domain. First, several local indicators are extracted from MPEG macroblocks, and AdaBoost is employed for feature selection and fusion. The selected features are then used in classifying candidate cuts into five sub-spaces via pre-filtering and rule-based decision making. Following that, global indicators of frame similarity between boundary frames of cut candidates are examined using phase correlation of dc images. Gradual transitions like fade, dissolve, and combined shot cuts are also identified. Experimental results on the test data from TRECVID'07 have demonstrated the effectiveness and robustness of our proposed methodology. * INSPEC o Controlled Indexing decision making , image segmentation , knowledge based systems , video coding o Non Controlled Indexing AdaBoost , MPEG videos , feature selection , global indicator , local indicator , rule-based decision making , shot boundary detection , video segmentation * Author Keywords Decision making , TRECVID , shot boundary detection (SBD) , video segmentation , video signal processing References 1. J. Yuan , H. Wang , L. Xiao , W. Zheng , J. L. F. Lin and B. Zhang "A formal study of shot boundary detection", IEEE Trans. Circuits Syst. Video Technol., vol. 17, pp. 168 2007. Abstract |Full Text: PDF (2789KB) 2. C. Grana and R. Cucchiara "Linear transition detection as a unified shot detection approach", IEEE Trans. Circuits Syst. Video Technol., vol. 17, pp. 483 2007. Abstract |Full Text: PDF (505KB) 3. Q. Urhan , M. K. Gullu and S. Erturk "Modified phase-correlation based robust hard-cut detection with application to archive film", IEEE Trans. Circuits Syst. Video Technol., vol. 16, pp. 753 2006. Abstract |Full Text: PDF (3808KB) 4. C. Cotsaces , N. Nikolaidis and I. Pitas "Video shot detection and condensed representation: A review", Proc. IEEE Signal Mag., vol. 23, pp. 28 2006. 5. National Institute of Standards and Technology (NIST), pp. [online] Available: http://www-nlpir.nist.gov/projects/trecvid/ 6. J. Bescos "Real-time shot change detection over online MPEG-2 video", IEEE Trans. Circuits Syst. Video Technol., vol. 14, pp. 475 2004. Abstract |Full Text: PDF (1056KB) 7. H. Lu and Y. P. Tan "An effective post-refinement method for shot boundary detection", IEEE Trans. Circuits Syst. Video Technol., vol. 15, pp. 1407 2005. Abstract |Full Text: PDF (3128KB) 8. G. Boccignone , A. Chianese , V. Moscato and A. Picariello "Foveated shot detection for video segmentation", IEEE Trans. Circuits Syst. Video Technol., vol. 15, pp. 365 2005. Abstract |Full Text: PDF (2152KB) 9. Z. Cernekova , I. Pitas and C. Nikou "Information theory-based shot cut/fade detection and video summarization", IEEE Trans. Circuits Syst. Video Technol., vol. 16, pp. 82 2006. Abstract |Full Text: PDF (1184KB) 10. L.-Y. Duan , M. Xu , Q. Tian , C.-S. Xu and J. S. Jin "A unified framework for semantic shot classification in sports video", IEEE Trans. Multimedia, vol. 7, pp. 1066 2005. Abstract |Full Text: PDF (2872KB) 11. H. Fang , J. M. Jiang and Y. Feng "A fuzzy logic approach for detection of video shot boundaries", Pattern Recogn., vol. 39, pp. 2092 2006. [CrossRef] 12. R. A. Joyce and B. Liu "Temporal segmentation of video using frame and histogram space", IEEE Trans. Multimedia, vol. 8, pp. 130 2006. Abstract |Full Text: PDF (864KB) 13. A. Hanjalic "Shot boundary detection: Unraveled and resolved", IEEE Trans. Circuits Syst. Video Technol., vol. 12, pp. 90 2002. Abstract |Full Text: PDF (289KB) 14. S.-C. Pei and Y.-Z. Chou "Efficient MPEG compressed video analysis using macroblock type information", IEEE Trans. Multimedia, vol. 1, pp. 321 1999. Abstract |Full Text: PDF (612KB) 15. C.-L. Huang and B.-Y. Liao "A robust scene-change detection method for video segmentation", IEEE Trans. Circuits Syst. Video Technol., vol. 11, pp. 1281 2001. Abstract |Full Text: PDF (241KB) 16. Y. Freund and R. E. Schapire "A decision-theoretic generalization of online learning and an application to boosting", J. Comput. Syst. Sci., vol. 55, pp. 119 1997. [CrossRef] On this page * Abstract * Index Terms * References Brought to you by STRATHCLYDE UNIVERSITY LIBRARY * Your institute subscribes to: * IEEE-Wiley eBooks Library , IEEE/IET Electronic Library (IEL) * What can I access? Terms of Us

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

An HMM-Based Framework for Video Semantic Analysis

Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis