Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper

Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very di#erent appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels

Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. Although the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic components, the shots, only few comparative investigations on early shot boundary detection algorithms have been published. These investigations mainly concentrate on measuring the edit detection performance, however, do not consider the algorithms ’ ability to classify the types and to locate the boundaries of the edits correctly. This paper extends these comparative investigations. More recent algorithms designed explicitly to detect specific complex editing operations such as fades and dissolves are taken into account, and their ability to classify the types and locate the boundaries of such edits are examined. The algorithms ’ performance is measured in terms of hit rate, number of false hits, and miss rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments show that while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rate for dissolves is usually unacceptably high, ranging from 50 % up to over 400%. Moreover, all algorithms seem to fail under roughly the same conditions.

ing Rainer Lienhart, Silvia Pfeiffer and Wolfgang Effelsberg University of Mannheim, 68131 Mannheim, Germany {lienhart, pfeiffer, effelsberg}@pi4.informatik.uni-mannheim.de 1. What is a Video Abstract? We all know what the abstract of an article is: a short summary of a document, often used to preselect material relevant to the user. The medium of the abstract and the document are the same, namely text. In the age of multimedia, it would be desirable to use video abstracts in very much the same way: as short clips containing the essence of a longer video, without a break in the presentation medium. However, the state of the art is to use textual abstracts for indexing and searching large video archives. This media break is harmful since it typically leads to considerable loss of information. For example it is unclear at what level of abstraction the textual description should be; if we see a famous politician at a dinner table with a group of other politicians, what should the text sa...

A fundamental task in video analysis is to extract structures from the video to facilitate user's access (browsing and retrieval). Motivated by the important role that Table-of-Content (ToC) plays in a book, in this paper we introduce the concept of ToC in the video domain. Some existing approaches implicitly use the ToC, but are mainly limited to low-level entities (e.g. shots and key frames). The drawbacks are that low-level structures (1) contain too many entries to be e ciently presented to the user � and (2) do not capture the underlying semantic structure of the video based on which the user may wishtobrowse/retrieve. To address these limitations, in this paper we present an e ective semantic-level ToC construction technique based on intelligent unsupervised clustering. It has the characteristics of better modeling the time locality and scene structure. Experiments based on real-world movie videos validate the e ectiveness of the proposed approach. Examples are given to demonstrate the usage of the scene based ToC in facilitating user's access to the video. Key words: video accessing, scene level ToC construction 1

This paper describes the theoretic framework and applications of automatic audio content analysis. After explaining the tools for audio analysis such as analysis of the pitch or the frequency spectrum, we describe new applications which can be developed using the toolset. We discuss content-based segmentation of the audio stream, music analysis and violence detection.

Abstract–In the future we envision systems that will provide video information delivery services to customers on a very large scale. These systems must provide customers with mechanisms to select programs of their choice from live broadcasts. Customers should also be provided with easy means of browsing and accessing pre-recorded digital data (e.g., distributed digital multimedia libraries), and downloading data from other information sources. To be viable for such large information sets, these systems must understand customer preferences and tailor the available information to the customer’s needs. To support this vision, a number of issues must be addressed and obstacles overcome. Intuitive interfaces, powerful query formulation and evaluation techniques, comprehensive data models, and flexible presentation functionalities must be developed. To realize these components, an effective query evaluation engine with the capabilities of query resolution in different content-specific formats (e.g., by graphics, by image, by sound) and in different domain-specific models (e.g., database of movies, database of newsclips) should be present. Additionally, the digital video database will require an efficient indexing system for easy access to the stored information. In this paper we discuss existing research trends in this

We have developed algorithms for automatic character segmentation in motion pictures which extract automatically and reliably the text in pre-title sequences, credit titles, and closing sequences with title and credits. The algorithms we propose make use of typical characteristics of text in videos in order to enhance segmentation and, consequently, recognition performance. As a result, we get segmented characters from video pictures. These can be parsed by any OCR software. The recognition results of multiple instances of the same character throughout subsequent frames are combined to enhance recognition result and to compute the final output. We have tested our segmentation algorithms in a series of experiments with video clips recorded from television and achieved good segmentation results. KEYWORDS: Character segmentation, character recognition, content-based indexing and retrieval, video processing 1 INTRODUCTION In the age of multimedia, video is an increasingly important and c...

TV commercials are interesting in many respects: advertisers and psychologists are interested in their influence on human purchasing habits, while parents might be interest in shielding their children from their influence. In this paper, two methods for detecting and extracting commercials in digital videos are described. The first method is based on statistics of measurable features and enables the detection of commercial blocks within TV broadcasts. The second method performs detection and recognition of known commercials with high accuracy. Finally, we show how both approaches can be combined into a self-learning system. Our experimental results underline the practicality of the methods.

. 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...