This paper presents methods for automatically creating pictorial video summaries that resemble comic books. The relative importance of video segments is computed from their length and novelty. Image and audio analysis is used to automatically detect and emphasize meaningful events. Based on this importance measure, we choose relevant keyframes. Selected keyframes are sized by importance, and then efficiently packed into a pictorial summary. We present a quantitative measure of how well a summary captures the salient events in a video, and show how it can be used to improve our summaries. The result is a compact and visually pleasing summary that captures semantically important events, and is suitable for printing or Web access. Such a summary can be further enhanced by including text captions derived from OCR or other methods. We describe how the automatically generated summaries are used to simplify access to a large collection of videos. 1.1 Keywords Video summarization and analysis, keyframe selection and layout. 2.

This paper presents a method for generating compact pictorial summarizations of video. We developed a novel approach for selecting still images from a video suitable for summarizing the video and for providing entry points into it. Images are laid out in a compact, visually pleasing display reminiscent of a comic book or Japanese manga. Users can explore the video by interacting with the presented summary. Links from each keyframe start video playback and/or present additional detail. Captions can be added to presentation frames to include commentary or descriptions such as the minutes of a recorded meeting. We conducted a study to compare variants of our summarization technique. The study participants judged the manga summary to be significantly better than the other two conditions with respect to their suitability for summaries and navigation, and their visual appeal.

Recent advances in digital video compression and networks have made video more accessible than ever. However, the existing content-based video retrieval systems still suffer from the following problems. 1 ) Semantics---sensitive video classification problem because of the semantic gap between low-level visual features and high-level semantic visual concepts; 2) Integrated video access problem because of the lack of efficient video database indexing, automatic video annotation, and concept-oriented summary organization techniques. In this paper, we have proposed a novel framework, called ClassView, to make some advances toward more efficient video database indexing and access. 1) A hierarchical semantics-sensitive video classifier is proposed to shorten the semantic gap. The hierarchical tree structure of the semantics-sensitive video classifier is derived from the domain-dependent concept hierarchy of video contents in a database. Relevance analysis is used for selecting the discriminating visual features with suitable importances. The Expectation-Maximization (EM) algorithm is also used to determine the classification rule for each visual concept node in the classifier. 2) A hierarchical video database indexing and summary presentation technique is proposed to support more effective video access over a large-scale database. The hierarchical tree structure of our video database indexing scheme is determined by the domain-dependent concept hierarchy which is also used for video classification. The presentation of visual summary is also integrated with the inherent hierarchical video database indexing tree structure. Integrating video access with efficient database indexing tree structure has provided great opportunity for supporting more powerful video search engines.

eo from a single camera running from camera on to camera off. Using one keyframe per shot means that representing a one-hour video usually requires hundreds of keyframes. In contrast, our approach for video indexing and summarization selects fewer keyframes that represent the entire video and index the interesting parts. The user can select the number of keyframes or the application can select the optimal number of keyframes based on display size, but a one-hour video typically will have between 10 and 40 keyframes. We use several techniques to present the automatically selected keyframes. A video directory listing shows one keyframe for each video and provides a slider that lets the user change the keyframes dynamically. The visual summary of a single video presents images in a compact, visually pleasing display. To deal with the large number of keyframes that represent clips in a video editing system, we group keyframes into piles based on their visual similarity. In all three inter

We can summarize a video using its 'important' and/or 'interesting' scenes. The scenes selected, however, depend on the purpose of the summary. A good summarizing technique should be able to support various summarization needs. In this paper, we introduceatechnique which allows the user to choose a few scene as important according to the application. Based on these selections, our algorithm automatically uncovers the remaining important scenes in the video. In this method, each scene is representedbyacouple of numerical values. Since the processing is basedonthese numbers, it is highly efficient. Our experimental results indicate that this approach performs accurately, and is suitable for large videoarchives. KEYWORDS: Video Summarization, Video Analysis, Video Browsing, Scene Change Detection. 1 Introduction The researches on video summarization have been done in twoways for different purposes. One way is to extract keyframe(s) from each shot or scene, and presentthemas the summa...

In this paper, we describe description schemes (DSs) for image, video, multimedia, home media, and archive content proposed to the MPEG-7 standard. MPEG-7 aims to create a multimedia content description standard in order to facilitate various multimedia searching and filtering applications. During the design process, special care was taken to provide simple but powerful structures that represent generic multimedia data. We use the eXtensible Markup Language (XML) to illustrate and exemplify the proposed DSs because of its interoperability and flexibility advantages. The main components of the image, video, and multimedia description schemes are object, feature classification, object hierarchy, entity-relation graph, code downloading, multi-abstraction levels, and modality transcoding. The home media description instantiates the former DSs proposing the 6-W semantic features for objects, and 1-P physical and 6-W semantic object hierarchies. The archive description scheme aims to describ...

Abstract. Digital video databases have become more pervasive and finding video clips quickly in large databases becomes a major challenge. Due to the nature of video, accessing contents of video is difficult and time-consuming. With content-based video systems today, there exists a significant gap between the user’s information and what the system can deliver. Therefore, enabling intelligent means of interpretation on visual content, semantics annotation and retrieval are important topics of research. In this paper, we consider semantic interpretation of the contents as annotation tags for video clips, giving a retrieval-driven and applicationoriented semantics extraction, annotation and retrieval model for video database management system. This system design employs an algorithm on objects ’ relation and it can reveal the semantics defined with fast real-time computation.

this document we use the eXtensible Markup Language (XML) [8] to explain the elements and structures of the proposed scheme. We also use UML notation to represent the structure of our description scheme graphically. The proposed MMDS has been developed to achieve the maximum synergy with our separate proposals for the image DS, the video DS, the audio DS, and the synthetic video DS [4] [5] [6] [7]. In the proposed MMDS, a multimedia stream is represented as a set of relevant multimedia objects can be further organized by using object hierarchies. Relationships among multiple multimedia objects that can not be expressed using a tree structure are described using entity relation graphs. Multimedia objects can include multiple features, each of which can contain multiple descriptors. Each descriptor can link to external feature extraction and similarity matching code. Features are grouped according to the following categories: media features, semantic features, and temporal features. At the same time, each multimedia object includes a set of single-media objects, which together form the multimedia object. Single-media objects are associated with features, hierarchies, entity 2 relation graphs, and multiple abstraction levels, as described by single media description schemes (image, video, etc.). We describe the design principles and goals in Section 2. The proposed multimedia description scheme is described in Section 3. Section 4 briefly explains the system we use to demonstrate the capabilities of the MMDS. Annex A includes the full DTD of the multimedia integration description scheme.

Tremendous growth of the Internet population creates a large demand on new applications, and advances in Internet technologies make it feasible to develop new exciting application base on video and broadband network. One of the most hottest topic nowadays is the Digital Video Library Systems. It has a promising application scope in entertainment, information, education or business. However, due to the temporal nature of video, indexing and retrieval of video content is not trivial. Therefore, we will introduce the digital video library network in this paper with a number of techniques concerning the indexing and retrieval of video contents. Contents Introduction ................................................................................................................... 3 System Architecture ...................................................................................................... 4 Video Server .........................................................................

In this paper, we present an effective framework for scene detection and structure analysis for sports videos, using tennis and baseball as examples. Sports video can be characterized by its predictable temporal syntax, recurrent events with consistent features, and a fixed number of views. Our approach combines domain-specific knowledge, supervised machine learning techniques, and automatic feature analysis at multiple levels. Real time processing performance is achieved by utilizing compressed-domain processing techniques. High accuracy in view recognition is achieved by using compressed-domain global features as prefilters and object-level refined analysis in the latter verification stage. Applications include high-level structure browsing/navigation, highlight generation, and mobile media tiltering.