Image retrieval based on visual information concepts and automatic image annotation

Image retrieval based on visual information concepts and automatic image annotation

Nowadays, we are living in the content-based image retrieval (CBIR) age. The users would like to give the semantic queries, but the semantic understanding of images remains an important research challenge for the image and video retrieval community. We have approached the CBIR at semantics level by using visual information concepts (VIC) and automatic image annotation (AIA). We have linked the semantic concepts to the image at two levels, the common level and the private level. In the common level, we used the VIC and linking automatically VIC to image data based on the priori knowledge. In the private level, we performed the AIA based on the cross-media relevance model with some improvements. The content image retrieval process is based on the comparison of the intermediate descriptor values in VIC associated with both the semantic data and the image data. Irrelevant images are rejected and the remaining images are ranked by AIA. Our experiment results have shown that the performance of our system is better in the meanings of precision and recall than the traditional systems only based query images or only based on VIC or AIA.