CBIR: From Low-Level Features to High-Level Semantics

The performance of a content-based image retrieval (CBIR) system is inherently constrained by the features adopted to represent the images in the database. Use of low-level features can not give satisfactory retrieval results in many cases; especially when the high-level concepts in the user’s mind is not easily expressible in terms of the low-level features. Therefore whenever possible, textual annotations shall be added or extracted and/or processed to improve the retrieval performance. In this paper a hybrid image retrieval system is presented to provide the user with the flexibility of using both the high-level semantic concept/keywords as well as low-level feature content in the retrieval process. The emphasis is put on a statistical algorithm for semantic grouping in the concept space through relevance feedback in the image space. Under this framework, the system can also incrementally learn the user’s search habit/preference in terms of semantic relations among concepts; and uses this information to improve the performance of subsequent retrieval tasks. This algorithm can eliminate the need for a stand-alone thesaurus, which may be too large in size and contain too much redundant information to be of practical use. Simulated experiments are designed to test the effectiveness of the algorithm. An intelligent dialog system, to which this algorithm can be a part of the knowledge acquisition module, is also described as a front end for the CBIR system.