System Analysis And Design For Multimedia Retrieval Systems
This work addresses the challenge of efficiently searching and browsing large multimedia collections over the web, but it appears incremental as it combines existing features without introducing a new method.
The paper tackles the problem of improving content-based video retrieval by proposing a system that uses multiple features (color histogram, texture, edge density, and motion) for indexing and retrieval, and it validates this approach through implementation, though no concrete performance numbers are provided.
Due to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. Content based video retrieval is an approach for facilitating the searching and browsing of large multimedia collections over WWW. In order to create an effective video retrieval system, visual perception must be taken into account. We conjectured that a technique which employs multiple features for indexing and retrieval would be more effective in the discrimination and search tasks of videos. In order to validate this, content based indexing and retrieval systems were implemented using color histogram, Texture feature (GLCM), edge density and motion..