Capturing image semantics with low-level descriptors

• Published in: 2001 International Conference on Image Processing Vol. 1; pp. 18 - 21 vol.1
• Main Authors: Mojsilovic, A., Rogowitz, B.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2001
• Subjects: Humans; Image analysis; Image databases; Image retrieval; Information retrieval; Multidimensional systems; Prototypes; Psychology; Spatial databases; Testing
• ISBN: 0780367251; 9780780367258
• DOI: 10.1109/ICIP.2001.958942

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.