: Including all the scenes/shots that contain special events may generate too long an abstract. Also, simply staggering them together may not be visually or aurally appealing. In the MoCA project, it was determined that only 50% of the abstract should contain special events. The remaining part should be left for filler clips. The special event clips to be included are chosen uniformly and randomly from different types of events. The selection of a short clip from a scene is subject to some additional criteria, such as the amount of action and the similarity to the overall color composition of the movie. Closeness to the desired AV characteristics of certain scene types are also considered. The filler clips are chosen so that they do not overlap with the content of chosen special event clips, to ensure a good coverage of all parts of a movie. MPEG-7 Standard for Multimedia Content Description Interface MPEG-7 is an on-going standardization effort for content description of AV documen...

Image and video indexing techniques are crucial in multimedia applications. A number of the indexing techniques that operate in the pixel domain have been reported in the literature. The advent of compression standards has led to the proliferation of indexing techniques in the compressed domain. In this paper, we present a critical review of the compressed domain indexing techniques proposed in the literature. These include transform domain techniques using Fourier transform, Cosine transform, Karhunen-Loeve transform, Subbands and Wavelets; and spatial domain techniques using Vector Quantization and Fractals. In addition, temporal indexing techniques using motion vectors are also discussed.

This paper provides an overview of current research in image information retrieval and provides an outline of areas for future research. The approach is broad and interdisciplinary and focuses on three aspects of image research (IR): text-based retrieval, content-based retrieval, and user interactions with image information retrieval systems. The review concludes with a call for image retrieval evaluation studies similar to TREC. Keywords: Information Science, Image Retrieval, CBIR, Introduction Interest in image retrieval has increased in large part due to the rapid growth of the World Wide Web. According to a recent study (Lawrence & Giles, 1999) there are 180 million images on the publicly indexable Web, a total amount of image data of about 3Tb [terabytes], and an astounding one million or more digital images are being produced every day (Jain, 93). The need to find a desired image from a collection is shared by many groups, including journalists, engineers, historians, designers...

Temporal video segmentation is the "rst step towards automatic annotation of digital video for browsing and retrieval. This article gives an overview of existing techniques for video segmentation that operate on both uncompressed and compressed video stream. The performance, relative merits and limitations of each of the approaches are comprehensively discussed and contrasted. The gradual development of the techniques and how the uncompressed domain methods were tailored and applied into compressed domain are considered. In addition to the algorithms for shot boundaries detection, the related topic of camera operation recognition is also reviewed. � 2001 Elsevier Science B.V. All rights reserved.

Abstract—In this paper, we describe a statistical video representation and modeling scheme. Video representation schemes are needed to segment a video stream into meaningful video-objects, useful for later indexing and retrieval applications. In the proposed methodology, unsupervised clustering via Gaussian mixture modeling extracts coherent space-time regions in feature space, and corresponding coherent segments (video-regions) in the video content. A key feature of the system is the analysis of video input as a single entity as opposed to a sequence of separate frames. Space and time are treated uniformly. The probabilistic space-time video representation scheme is extended to a piecewise GMM framework in which a succession of GMMs are extracted for the video sequence, instead of a single global model for the entire sequence. The piecewise GMM framework allows for the analysis of extended video sequences and the description of nonlinear, nonconvex motion patterns. The extracted space-time regions allow for the detection and recognition of video events. Results of segmenting video content into static versus dynamic video regions and video content editing are presented. Index Terms—Video representation, video segmentation, detection of events in video, Gaussian mixture model. 1

: Efficient access to information contained in video databases implies that a structured representation of the content of the video is built beforehand. This paper describes an approach in this direction, targeted at video indexing and browsing. Exploiting a 2D motion model estimator, we partition the video into shots, characterize camera motion, extract and track mobile objects. These steps rely on robust motion estimation, statistical tests and contextual statistical labeling. The content of each shot can then be viewed on a synoptic frame composed of a mosaic image of the background scene, on which trajectories of mobile objects are superimposed. The proposed method also provides instantaneous and long-term, qualitative and quantitative object motion cues for content-based indexing. Its different steps and the system they form are designed to keep computational cost low, while being able to cope with general video content was aimed at. We provide experimental results on real-world s...

Abstract. This paper proposes a general framework to evaluate and compare several temporal video segmentation algorithms. The problems that must be solved to confront di erent methods summarize as gathering a common content set to test the methods, building a reference segmentation, establishing the rules to match the results with the reference, and providing a quality measure. Some solutions to these problems are given in this study and are applied for evaluating di erent methods developed in various contexts. The paper concludes by presenting results obtained on practical tests. This study was made by awork group of AIM (Multimedia Indexation Action) Working Group 10 of the ISIS Coordinated Research Program. 1

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Multimedia data are generally stored in compressed form in order to efficiently utilize the available storage facilities. Access to multimedia archives is thus dependent on our ability to browse compressed information. In this paper, a novel approach to multiple object tracking from compressed multimedia databases is presented. This approach is intended to operate in a distributed environment, where users initiate video searches and retrieve relevant video information simultaneously from multiple compressed video archives. The system operates on the compressed video to find and track objects of interest and determine their positions in the image. This enables more complex query formulations in terms of the relative positions of the target objects in the image. The filtering and analysis of motion information (motion vectors) is used to track objects in the video bit stream. Once the search has terminated, the system may decompress and display the query-relevant video sequences upon request.

Effective and efficient representation of color features of multiple video frames or pictures is an im- portant yet challenging task for visual information management systems. Key frame-based methods to represent the color features of a group of frames (GoF) are highly dependent on the selection crite- rion of the representative frame(s), and may lead to unreliable results. In this paper, we present various histogram-based color descriptors to reliably capture and represent the color properties of multiple images or a GoF. One family of such descriptors, called alpha-trimmed average histograms, combine individual frame or image histograms using a specific filtering operation to generate robust color histograms that can eliminate the adverse effects of brightness/color variations, occlusion, and edit effects on the color representation. We show the efficacy of the alpha-trimmed average histograms for video segment retrieval applications, and illustrate how they consistently outperform key frame-based methods. Another color histogram descriptor that we introduce, called the intersection histogram, reflects the number of pixels of a given color that is common to all the frames in the GoF. We employ the intersection histogram to develop a fast and efficient algorithm for identification of the video segment to which a query frame belongs. The proposed color histogram descriptors have been included in the recently completed ISO standard MPEG-7 after extensive evaluation experiments.