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.

Due to variations in illumination conditions in texture evaluation applications, gray scale invariance is an important property in texture similarity evaluation. Using the order of the gray values instead of the gray values themselves is shown to improve the retrieval accuracy. Ordinal measures have been used for many image processing tasks in the literature. In this paper, we propose a novel combination of ordinal measures and cooccurrence matrices using local thresholding. Features constructed in this paper represent the occurrence frequency of certain ordinal relationships at different distances and orientations. The proposed method gives encouraging results when comparing its retrieval performance to that of the rotation invariant local binary pattern approach and traditional gray level cooccurrence matrices. 1.