Combining Visual Semantics and Texture Characterizations for Precision-Oriented Automatic Image Retrieval [chapter]

Mohammed Belkhatir
2005 Lecture Notes in Computer Science  
The growing need for 'intelligent' image retrieval systems leads to new architectures combining visual semantics and signal features that rely on highly expressive frameworks while providing fully-automated indexing and retrieval processes. Indeed, addressing the issue of integrating the two main approaches in the image indexing and retrieval literature (i.e. signal and semantic) is a viable solution for achieving significant retrieval quality. This paper presents a multi-facetted framework
more » ... uring visual semantics and signal texture descriptions for automatic image retrieval. It relies on an expressive representation formalism handling high-level image descriptions and a full-text query framework in an attempt to operate image indexing and retrieval operations beyond trivial low-level processes and loosely-coupled state-of-the-art systems. At the experimental level, we evaluate the retrieval performance of our system through recall and precision indicators on a test collection of 2500 photographs used in several world-class publications.
doi:10.1007/978-3-540-31865-1_33 fatcat:or6o2xsthjcbxc2x5vxc7dtbsa