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

MUVIS is a series of CBIR systems. The first one 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. MUVIS has been reformed to become a PC-based framework, which supports indexing, browsing and querying of various multimedia types such as audio. video, audiohideo interlaced and several image formats. MUVIS system allows real-time audio and video capturing, encoding by last generation codecs such as MPEG-4. H.263+. MP3 and AAC. It supports several audiohideo file format such as AVI, MP4, MP3 and AAC. Furthermore. MWIS system provides a well-defined interface for third parties to integrate their own feature extraction algorithms into the framework and for this reason it has recently been adopted by COST 21 lquat as COST framework for CBR. In this paper. we describe the general system features with underlying applications and outline the main philosophy. 1.

MUVIS is a PC-based framework, which supports indexing, browsing and querying of various multimedia types such as audio, video, audio/video interlaced in several formats. It allows real-time audio and video capturing, encoding by last generation codecs such as MPEG-4, H.263+, MP3 and AAC. MUVIS also supports several audio/video file format such as AVI, MP4, MP3 and AAC. Almost all image types in a PC environment including JPEG-2000 can be rendered, indexed and converted within MUVIS framework. Along with such a wide multimedia coverage, MUVIS has been developed to achieve a global and unified solution for content-based indexing and retrieval problem and to provide user-friendly applications and a generic framework especially for third parties to develop their feature extraction modules. In this paper, we present an overview of the MUVIS system and we shall especially focus on the overall audio-based multimedia indexing and retrieval scheme within MUVIS framework. 1.