Content-based Digital Video Processing. Digital Videos Segmentation, Retrieval and Interpretation.

Recent research approaches in semantics based video content analysis require shot boundary detection as the first step to divide video sequences into sections. Furthermore, with the advances in networking and computing capability, efficient retrieval of multimedia data has become an important issue. Content-based retrieval technologies have been widely implemented to protect intellectual property rights (IPR). In addition, automatic recognition of highlights from videos is a fundamental and challenging problem for content-based indexing and retrieval applications. In this thesis, a paradigm is proposed to segment, retrieve and interpret digital videos. Five algorithms are presented to solve the video segmentation task. Firstly, a simple shot cut detection algorithm is designed for real-time implementation. Secondly, a systematic method is proposed for shot detection using content-based rules and FSM (finite state machine). Thirdly, the shot detection is implemented using local and global indicators. Fourthly, a context awareness approach is proposed to detect shot boundaries. Fifthly, a fuzzy logic method is implemented for shot detection. Furthermore, a novel analysis approach is presented for the detection of video copies. It is robust to complicated distortions and capable of locating the copy of segments inside original videos. Then, iv objects and events are extracted from MPEG Sequences for Video Highlights Indexing and Retrieval. Finally, a human fighting detection algorithm is proposed for movie annotation

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

Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos

In this paper, a content-aware approach is proposed to design multiple test conditions for shot cut detection, which are organized into a multiple phase decision tree for abrupt cut detection and a finite state machine for dissolve detection. In comparison with existing approaches, our algorithm is characterized with two categories of content difference indicators and testing. While the first category indicates the content changes that are directly used for shot cut detection, the second category indicates the contexts under which the content change occurs. As a result, indications of frame differences are tested with context awareness to make the detection of shot cuts adaptive to both content and context changes. Evaluations announced by TRECVID 2007 indicate that our proposed algorithm achieved comparable performance to those using machine learning approaches, yet using a simpler feature set and straightforward design strategies. This has validated the effectiveness of modelling of content-aware indicators for decision making, which also provides a good alternative to conventional approaches in this topic

Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels

Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances

An architecture for mining resources complementary to audio-visual streams

In this paper we attempt to characterize resources of information complementary to audio-visual (A/V) streams and propose their usage for enriching A/V data with semantic concepts in order to bridge the gap between low-level video detectors and high-level analysis. Our aim is to extract cross-media feature descriptors from semantically enriched and aligned resources so as to detect finer-grained events in video.We introduce an architecture for complementary resource analysis and discuss domain dependency aspects of this approach related to our domain of soccer broadcasts

A Fuzzy Logic-Based System for Soccer Video Scenes Classification

Massive global video surveillance worldwide captures data but lacks detailed activity information to flag events of interest, while the human burden of monitoring video footage is untenable. Artificial intelligence (AI) can be applied to raw video footage to identify and extract required information and summarize it in linguistic formats. Video summarization automation usually involves text-based data such as subtitles, segmenting text and semantics, with little attention to video summarization in the processing of video footage only. Classification problems in recorded videos are often very complex and uncertain due to the dynamic nature of the video sequence and light conditions, background, camera angle, occlusions, indistinguishable scene features, etc. Video scene classification forms the basis of linguistic video summarization, an open research problem with major commercial importance. Soccer video scenes present added challenges due to specific objects and events with similar features (e.g. “people” include audiences, coaches, and players), as well as being constituted from a series of quickly changing and dynamic frames with small inter-frame variations. There is an added difficulty associated with the need to have light weight video classification systems working in real time with massive data sizes. In this thesis, we introduce a novel system based on Interval Type-2 Fuzzy Logic Classification Systems (IT2FLCS) whose parameters are optimized by the Big Bang–Big Crunch (BB-BC) algorithm, which allows for the automatic scenes classification using optimized rules in broadcasted soccer matches video. The type-2 fuzzy logic systems would be unequivocal to present a highly interpretable and transparent model which is very suitable for the handling the encountered uncertainties in video footages and converting the accumulated data to linguistic formats which can be easily stored and analysed. Meanwhile the traditional black box techniques, such as support vector machines (SVMs) and neural networks, do not provide models which could be easily analysed and understood by human users. The BB-BC optimization is a heuristic, population-based evolutionary approach which is characterized by the ease of implementation, fast convergence and low computational cost. We employed the BB-BC to optimize our system parameters of fuzzy logic membership functions and fuzzy rules. Using the BB-BC we are able to balance the system transparency (through generating a small rule set) together with increasing the accuracy of scene classification. Thus, the proposed fuzzy-based system allows achieving relatively high classification accuracy with a small number of rules thus increasing the system interpretability and allowing its real-time processing. The type-2 Fuzzy Logic Classification System (T2FLCS) obtained 87.57% prediction accuracy in the scene classification of our testing group data which is better than the type-1 fuzzy classification system and neural networks counterparts. The BB-BC optimization algorithms decrease the size of rule bases both in T1FLCS and T2FLCS; the T2FLCS finally got 85.716% with reduce rules, outperforming the T1FLCS and neural network counterparts, especially in the “out-of-range data” which validates the T2FLCSs capability to handle the high level of faced uncertainties. We also presented a novel approach based on the scenes classification system combined with the dynamic time warping algorithm to implement the video events detection for real world processing. The proposed system could run on recorded or live video clips and output a label to describe the event in order to provide the high level summarization of the videos to the user

Content-based video copy detection using multimodal analysis

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 67-76.Huge and increasing amount of videos broadcast through networks has raised the need of automatic video copy detection for copyright protection. Recent developments in multimedia technology introduced content-based copy detection (CBCD) as a new research field alternative to the watermarking approach for identification of video sequences. This thesis presents a multimodal framework for matching video sequences using a three-step approach: First, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In the second step, a spatiotemporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Finally the non-facial shots are matched using low-level visual features. In addition, we utilize fuzzy logic approach for extracting color histogram to detect shot boundaries of heavily manipulated video clips. Methods for detecting noise, frame-droppings, picture-in-picture transformation windows, and extracting mask for still regions are also proposed and evaluated. The proposed method was tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results were compared with the results of top-8 most successful techniques submitted to this task. Experimental results show that the proposed method performs better than most of the state-of-the-art techniques, in terms of both effectiveness and efficiency.Küçüktunç, OnurM.S