MUVIS: a system for content-based image retrieval
Faouzi Alaya Cheikh · 2004
The thesis considers different aspects of the development of a system called MU-VIS 1 developed in the MuVi 2 project for content-based indexing and retrieval in large image databases. The issues discussed are system design, graphical user interface design, shape features extraction and their corresponding similarity measures, and the use of relevance feedback to enhance the retrieval performance. Query by content, or content-based retrieval has recently been proposed as an alternative to text-based retrieval for media such as images, video and audio. Text-based retrieval is no longer appropriate for indexing such media, for several reasons. Firstly, keyword annotation is labor intensive, and it is not even possible when large sets of images are to be indexed. Secondly, these annotations are drawn from a predefined set of keywords which cannot cover all possible concepts images may represent. Finally, keywords assignment is subjective to the person making it. Therefore, content-based image retrieval (CBIR) systems propose to index the media documents based on features extracted from their content rather