Evaluating and combining digital video shot boundary detection algorithms

The development of standards for video encoding coupled with the increased power of computing mean that content-based manipulation of digital video information is now feasible. Shots are a basic structural building block of digital video and the boundaries between shots need to be determined automatically to allow for content-based manipulation. A shot can be thought of as continuous images from one camera at a time. In this paper we examine a variety of automatic techniques for shot boundary detection that we have implemented and evaluated on a baseline of 720,000 frames (8 hours) of broadcast television. This extends our previous work on evaluating a single technique based on comparing colour histograms. A description of each of our three methods currently working is given along with how they are evaluated. It is found that although the different methods have about the same order of magnitude in terms of effectiveness, different shot boundaries are detected by the different methods. We then look at combining the three shot boundary detection methods to produce one output result and the benefits in accuracy and performance that this brought to our system. Each of the methods were changed from using a static threshold value for three unconnected methods to one using three dynamic threshold values for one connected method. In a final summing up we look at the future directions for this work

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

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Evaluation of automatic shot boundary detection on a large video test suite

The challenge facing the indexing of digital video information in order to support browsing and retrieval by users, is to design systems that can accurately and automatically process large amounts of heterogeneous video. The segmentation of video material into shots and scenes is the basic operation in the analysis of video content. This paper presents a detailed evaluation of a histogram-based shot cut detector based on eight hours of TV broadcast video. Our observations are that the selection of similarity thresholds for determining shot boundaries in such broadcast video is difficult and necessitates the development of systems that employ adaptive thresholding in order to address the huge variation of characteristics prevalent in TV broadcast video

Highly efficient low-level feature extraction for video representation and retrieval.

PhDWitnessing the omnipresence of digital video media, the research community has raised the question of its meaningful use and management. Stored in immense multimedia databases, digital videos need to be retrieved and structured in an intelligent way, relying on the content and the rich semantics involved. Current Content Based Video Indexing and Retrieval systems face the problem of the semantic gap between the simplicity of the available visual features and the richness of user semantics. This work focuses on the issues of efficiency and scalability in video indexing and retrieval to facilitate a video representation model capable of semantic annotation. A highly efficient algorithm for temporal analysis and key-frame extraction is developed. It is based on the prediction information extracted directly from the compressed domain features and the robust scalable analysis in the temporal domain. Furthermore, a hierarchical quantisation of the colour features in the descriptor space is presented. Derived from the extracted set of low-level features, a video representation model that enables semantic annotation and contextual genre classification is designed. Results demonstrate the efficiency and robustness of the temporal analysis algorithm that runs in real time maintaining the high precision and recall of the detection task. Adaptive key-frame extraction and summarisation achieve a good overview of the visual content, while the colour quantisation algorithm efficiently creates hierarchical set of descriptors. Finally, the video representation model, supported by the genre classification algorithm, achieves excellent results in an automatic annotation system by linking the video clips with a limited lexicon of related keywords

Automatic indexing of video content via the detection of semantic events

The number, and size, of digital video databases is continuously growing. Unfortunately, most, if not all, of the video content in these databases is stored without any sort of indexing or analysis and without any associated metadata. If any of the videos do have metadata, then it is usually the result of some manual annotation process rather than any automatic indexing. Thus, locating clips and browsing content is difficult, time consuming and generally inefficient. The task of automatically indexing movies is particularly difficult given their innovative creation process and the individual style of many film makers. However, there are a number of underlying film grammar conventions that are universally followed, from a Hollywood blockbuster to an underground movie with a limited budget. These conventions dictate many elements of film making such as camera placement and editing. By examining the use of these conventions it is possible to extract information about the events in a movie. This research aims to provide an approach that creates an indexed version of a movie to facilitate ease of browsing and efficient retrieval. In order to achieve this aim, all of the relevant events contained within a movie are detected and classified into a predefined index. The event detection process involves examining the underlying structure of a movie and utilising audiovisual analysis techniques, supported by machine learning algorithms, to extract information based on this structure. The result is an indexed movie that can be presented to users for browsing/retrieval of relevant events, as well as supporting user specified searching. Extensive evaluation of the indexing approach is carried out. This evaluation indicates efficient performance of the event detection and retrieval system, and also highlights the subjective nature of video content

Identification, indexing, and retrieval of cardio-pulmonary resuscitation (CPR) video scenes of simulated medical crisis.

Medical simulations, where uncommon clinical situations can be replicated, have proved to provide a more comprehensive training. Simulations involve the use of patient simulators, which are lifelike mannequins. After each session, the physician must manually review and annotate the recordings and then debrief the trainees. This process can be tedious and retrieval of specific video segments should be automated. In this dissertation, we propose a machine learning based approach to detect and classify scenes that involve rhythmic activities such as Cardio-Pulmonary Resuscitation (CPR) from training video sessions simulating medical crises. This applications requires different preprocessing techniques from other video applications. In particular, most processing steps require the integration of multiple features such as motion, color and spatial and temporal constrains. The first step of our approach consists of segmenting the video into shots. This is achieved by extracting color and motion information from each frame and identifying locations where consecutive frames have different features. We propose two different methods to identify shot boundaries. The first one is based on simple thresholding while the second one uses unsupervised learning techniques. The second step of our approach consists of selecting one key frame from each shot and segmenting it into homogeneous regions. Then few regions of interest are identified for further processing. These regions are selected based on the type of motion of their pixels and their likelihood to be skin-like regions. The regions of interest are tracked and a sequence of observations that encode their motion throughout the shot is extracted. The next step of our approach uses an HMM classiffier to discriminate between regions that involve CPR actions and other regions. We experiment with both continuous and discrete HMM. Finally, to improve the accuracy of our system, we also detect faces in each key frame, track them throughout the shot, and fuse their HMM confidence with the region\u27s confidence. To allow the user to view and analyze the video training session much more efficiently, we have also developed a graphical user interface (GUI) for CPR video scene retrieval and analysis with several desirable features. To validate our proposed approach to detect CPR scenes, we use one video simulation session recorded by the SPARC group to train the HMM classifiers and learn the system\u27s parameters. Then, we analyze the proposed system on other video recordings. We show that our approach can identify most CPR scenes with few false alarms

Adaptive Edge-Oriented Shot Boundary Detection

We study the problem of video shot boundary detection using an adaptive edge-oriented framework. Our approach is distinct in its use of multiple multilevel features in the required processing. Adaptation is provided by a careful analysis of these multilevel features, based on shot variability. We consider three levels of adaptation: at the feature extraction stage using locally-adaptive edge maps, at the video sequence level, and at the individual shot level. We show how to provide adaptive parameters for the multilevel edge-based approach, and how to determine adaptive thresholds for the shot boundaries based on the characteristics of the particular shot being indexed. The result is a fast adaptive scheme that provides a slightly better performance in terms of robustness, and a five fold efficiency improvement in shot characterization and classification. The reported work has applications beyond direct video indexing, and could be used in real-time applications, such as in dynamic monitoring and modeling of video data traffic in multimedia communications, and in real-time video surveillance. Experimental results are included