Semantic metric for image library exploration

We propose a method for semantic categorization and retrieval of photographic images based on low-level image descriptors derived from perceptual experiments. The method applies multidimensional scaling and hierarchical clustering analysis to identify candidate semantic categories into which human observers organize images. Through a series of subjective experiments we refine our definition of these categories and select a set of low-level image features that uniquely describe them. We then devise a new image similarity metric and develop a prototype system, which identifies the semantic category of the image and retrieves similar images from the database. We tested the metric on a set of new images and compared the categorization results with that of human observers. Our results provide a good match to human performance, thus validating the use of human judgments to develop semantic descriptors. Our method can be used for the enhancement of current retrieval methods, better organization of image/video databases, and the development of more intuitive navigation schemes, browsing methods and user interfaces.