Content Based Image Retrieval using

This paper demonstrates an approach to content based image retrieval founded on the semantically meaningful labelling of images by high level visual categories. The image labelling is achieved by means of a set of trained neural network classi-ers which map segmented image region descriptors onto semantic class membership terms. It is argued that the semantic terms give a good estimate of the salient features which are important for discrimination in image retrieval. Furthermore, it is shown that the choice of visual categories such as grass or sky which mirror high level human perception allows the implementation of intuitive and versatile query composition interfaces and a variety of image similarity metrics for content based retrieval.