Abstract
We present an information fusion approach to designing a semantics-sensitive image retrieval system. Our approach is based on the three different types of classifiers, which extract and provide semantic cues of image regions. This local information from the proposed classifiers is fused together to generate the semantic labels of images. The experimental results show that our information fusion approach makes image retrieval more semantics-sensitive and improves the image retrieval performance significantly.