The Virtual Image in Streaming Video Indexing

2002

Current advances in multimedia technology enable ease of capturing and encoding digital video. As a result, video data is rapidly growing and becoming very important in our life. It is because video can transfer a large amount of knowledge by providing combination of text, graphics, or even images. Despite the vast growth of video, the effectiveness of its usage is very limited due to the lack of a complete technology for the organization and retrieval of video data. To date, there is no "perfect" solution for a complete video data-management technology, which can fully capture the content of video and index the video parts according to the contents, so that users can intuitively retrieve specific video segments. We have found that successful content-based video datamanagement systems depend on three most important components: key-segments extraction, content descriptions and video retrieval. While it is almost impossible for current computer technology to perceive the content of the video to identify correctly its key-segments, the system can understand more accurately the content of a specific video type by identifying the typical events that happens just before or after the key-segments (specific-domainapproach). Thus, we have proposed a concept of customisable video segmentation module, which integrates the suitable segmentation techniques for the current type of video. The identified key-segments are then described using standard video content descriptions to enable content-based retrievals. For retrieval, we have implemented XQuery, which currently is the most recent XML query language and the most powerful compared to older languages, such as XQL and XML-QL.

2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 2018

International Journal of Digital Content: Technology and its Applications, 2009

The ultimate challenge of the semantic multimedia database research is to provide a system that can retrieve the most relevant and semantically accurate multimedia data (image, audio, text) according to the user requirement. In order to cope with this challenge there is a need for a system that can efficiently and effectively index the multimedia data. This paper provides an overview of multimodal video indexing. It cover different aspects and issues in video indexing and the approaches that are used. The ultimate main challenge is to mind the semantic gap. The structure of this paper is as follow. The following section outlines the introduction. The section-II brings multimodal analysis of videos. Section-III is about semantic indexes. Section-IV covers content based video indexing. Section-V is about different indexing techniques that are used. The paper ends with the major research issues and problem and last section conclusion about this paper.

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003

There are primarily two MUVIS phases. In the first phase a Web-based application has been developed in late 90s to support indexing and retrieval in large image databases using visual and semantic features such as color, texture and shape. During recent years, a new framework, which aims to bring a unified and global approach on indexing, browsing and querying of various digital multimedia types such as audio, video and image has been developed. This new system provides tools for real-time audio and video capturing, encoding by last generation codecs such as MPEG-4, H.263+, MP3 and AAC. It supports several digital image types including JPEG 2000 and furthermore, it provides a well-defined interface for third parties to integrate their own feature extraction algorithms into the framework. In this paper, we describe the general system features with underlying applications and outline the main philosophy.

Content-Based Multimedia Indexing (CBMI) is the multi-disciplinary field broadly aiming at extracting knowledge from multimedia information and providing fluent access to it. CBMI research proposes to develop methods to model, abstract and organise multimedia data in view of fast, effective and accurate access. CBMI covers all the chain from the data to the user. It encompasses a large variety of challenges from multimedia information capture and representation via content understanding and information fusion to effective information indexing and retrieval. Since the early days, CBMI research has therefore produced a wide range of fundamental and application-oriented contributions and has provided methodological guidelines for progressing in the field. Due to its multi-disciplinary nature, CBMI research has also strongly been impacted by progress in adjunct domains such as machine learning, large-scale information storage and processing, and new modes of user interaction.

Multimedia Tools and Applications, 2015

Multimedia indexing systems aim at providing easy, fast and accurate access to large multimedia repositories. Research in Content-Based Multimedia Indexing covers a wide spectrum of topics in content analysis, content description, content adaptation and content retrieval. Various tools and techniques from different fields such as data indexing, machine learning, pattern recognition, image analysis and human computer interaction have contributed to the success of multimedia systems. Although, there has been significant progress in the field, we still face situations when the system show limits in accuracy, generality and scalability. Hence, the goal of this special issue is to bring forward the recent advancements in content-based multimedia indexing. We received 48 submissions, but only accepted 17 (35.42 %), according to the review process that was very rigorous for this special issue and consisted of several rounds of review. The high number of submissions reflect the importance and timeliness of the research field on content-based multimedia indexing. The selected 17 papers in this special issue are high-class publications and cover a wide range of problems in content indexing of multimedia data. The first paper BVariability modelling for audio events detection in movies^(

Faculty of Science and Technology, 2002

Current advances in multimedia technology enable ease of capturing and encoding digital video. As a result, video data is rapidly growing and becoming very important in our life. It is because video can transfer a large amount of knowledge by providing combination of text, graphics, or even images. Despite the vast growth of video, the effectiveness of its usage is very limited due to the lack of a complete technology for the organization and retrieval of video data. To date, there is no "perfect" solution for a complete video data-management technology, which can fully capture the content of video and index the video parts according to the contents, so that users can intuitively retrieve specific video segments. We have found that successful content-based video datamanagement systems depend on three most important components: key-segments extraction, content descriptions and video retrieval. While it is almost impossible for current computer technology to perceive the content of the video to identify correctly its key-segments, the system can understand more accurately the content of a specific video type by identifying the typical events that happens just before or after the key-segments (specific-domainapproach). Thus, we have proposed a concept of customisable video segmentation module, which integrates the suitable segmentation techniques for the current type of video. The identified key-segments are then described using standard video content descriptions to enable content-based retrievals. For retrieval, we have implemented XQuery, which currently is the most recent XML query language and the most powerful compared to older languages, such as XQL and XML-QL.

Multimedia Tools and Applications

Content-based multimedia indexing systems aim at providing user-friendly, fast and accurate access to large multimedia repositories from the automatic analysis of multimedia content, so as to find specific content, information, gain knowledge and insight. Over the years, various tools and techniques from different fields such as database, machine learning, pattern recognition, and human computer interaction have contributed to the success of multimedia systems. In spite of significant progress in the field and of the Bdeep learning revolution^, we still face situations where multimedia systems analyzing content show limits in accuracy, generality and scalability. This special issue focuses on the recent advances in content-based multimedia indexing core technology-e.g., understanding deep learning architectures, using formal knowledge and ontologies, putting the user in the loop-and in its applications of the latter. Interestingly, contributions address a variety of real-life applications such as human action recognition, cataract surgery videos, visual attention of people with dementia and TV program related applications. This special issue of Multimedia Tools and Applications follows the 14th edition of the Content-Based Multimedia Indexing (CBMI) workshop, held in Bucharest in the spring of 2016. After its first edition in Toulouse, France, in 1999, CBMI has progressively evolved as the main venue to discuss progress and trends in the field. The workshop is now a wellestablished forum that brings together the various communities involved in all aspects of content-based multimedia indexing for information retrieval, browsing, visualization and for multimedia analytics at large. The 2016 workshop received 65 submissions, from which the 60% most qualified were selected to be presented at the conference, covering all aspects of the field and drawing a clear picture of recent achievements and current trends.

International Journal of Computer Applications, 2012

Retrieval of multimedia has become a requirement for many contemporary information systems. These systems need to provide browsing, querying, navigation, and, sometimes, composition capabilities involving various forms of media. In this survey, we review techniques for text, image, audio and video retrieval. We first look at indexing and retrieval techniques for text, audio, image and video. We also discuss features visual features for video retrieval such as colour, texture, shape. The indexing techniques are discussed for these features. We also compare most popular techniques used for indexing and retrieval.

1998

Abstract: This paper describes an approach to the design and implementation of a video content management system that enables the efficient indexing and storing, and searching and browsing of video segments defined by annotation of video content attributes. The ...