Multimedia database

About: Multimedia database is a research topic. Over the lifetime, 1404 publications have been published within this topic receiving 19856 citations. The topic is also known as: Multimedia database & MMDB.

Content-Based Image and Video Retrieval

Abstract: Content based image and video retrieval is a active field of research in computer vision and multimedia database management. This paper reviews the current state of the art in content based image and video retrieval. For still images, we focus on the retrieval methods based on interactive feedback as well as image feature information such as color, texture, shape, region and object. For video sequences, we introduce such techniques as shot detection, representation of shot content, semantic scene description. Finally, the difficulty and the future work in this research field is pointd out.

Intelligent search and retrieval of a large multimedia knowledge base for the Hubble Space Telescope

Abstract: A document-retrieval assistant (DRA) in a microcomputer format is described which incorporates hypertext and natural language capabilities Hypertext is used to introduce an intelligent search capability, and the natural-language interface permits access to specific data without the use of keywords The DRA can be used to access and 'browse' the large multimedia database that is composed of project documentation from the HST

The spatial indexing method for supporting a circular location property of object

Abstract: To increase the retrieval performance in spatial and multimedia database systems, it is required to develop spatial indexing methods considering the spatial locality. The spatial locality is related to the location property of objects. Most spatial indexing methods, however, were not considered the circular location property of objects. In this paper, we propose a dynamic spatial index structure, called CR*-tree. It is a new spatial index structure to support the circular location property of objects in which a search space is organized with the circular and linear domains. We include the performance test results that verify this advantage of the CR*-tree and show that the CR*-tree outperforms the R*-tree.

Using editing operations to improve searching by color in multimedia database systems

Abstract: Since multimedia database management systems determine similarity by comparing sets of image features, relevant images in the database can be missed if their features do not match those extracted from the query image. Many failed matches can be avoided if modified versions of the missed relevant images are also stored in the underlying database. To minimize the storage cost associated with adding extra images to the database, the modified versions can be stored as sequences of editing operations instead of as large, binary objects. This article presents a technique for processing color-based queries in this environment that accesses the sequences of editing operations directly. It also presents a methodology that can be used to speed up the query processing just as ordered indices speed up the processing of traditional queries. In addition, this article provides a performance illustrating the technique's strengths and weaknesses when compared with the traditional approach to processing color-based queries. The results indicate that with low similarity thresholds, the proposed technique processes similarity searches more accurately than the traditional approach while using less database storage space since the modified versions are kept as editing operation sequences. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 182–194, 2008

A Dynamic Illustration Approach For Arabic Text

Abstract: In this paper, we present an approach to dynamically transform simple Modern Standard Arabic children’s stories scripts to the best representative images that can illustrate efficiently the meaning of words and word senses. We connect formally multiple datasets involved in our framework. We then apply several techniques to find the images and associate them with related word senses. First, we apply natural language processing techniques to analyze the text in stories and we build a semantic representation of main characters and events in each paragraph. Second, we apply various query formulation techniques as a brief scenario to enhance image web search which showed better accuracy as per preliminary results. Third, most significant queries are chosen to retrieve a list of possible candidate images from our multimedia database and search engines (i.e., Google and Bing). Instructors can then select and validate the ranked contextual images to compose the final visualization for each paragraph.