Abstract:
We propose a method for semantic categorization and retrieval of photographic images based on low-level image descriptors. In this method, we first use multidimensional s...Show MoreMetadata
Abstract:
We propose a method for semantic categorization and retrieval of photographic images based on low-level image descriptors. In this method, we first use multidimensional scaling (MDS) and hierarchical cluster analysis (HCA) to model the semantic categories into which human observers organize images. Through a series of psychophysical experiments and analyses, we refine our definition of these semantic categories, and use these results to discover a set of low-level image features to describe each category. We then devise an image similarity metric that embodies our results, and develop a prototype system, which identifies the semantic category of the image and retrieves the most similar images from the database. We tested the metric on a new set of 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.
Date of Conference: 07-10 October 2001
Date Added to IEEE Xplore: 07 August 2002
Print ISBN:0-7803-6725-1