Human perception-driven, similarity-based access to image databases

Celebi, M. E., and Aslandogan, Y. A. Human perception-driven, similarity-based access to image databases. In Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (Clearwater Beach, Florida, May 15–17 2005), I. Russell and Z. Markov, Eds., pp. 245–251. [PDF]


In this work, the author used human perception of similarity as a guide in optimizing an image distance function in a content-based image retrieval system. A psychophysical experiment was designed to measure the perceived similarity of each image with every other image in the database. The weights of the distance function were optimized by means of a genetic algorithm using the distance matrix obtained from subjective experiments. Using the optimized distance function, the retrieval performance of the system was significantly improved.

In this study, the authors focused on shape similarity. However, the authors argue that the same approach can be used to develop similarity functions based on other low-level features such as color or texture.

This paper contains relevant references of image retrieval systems trained over human perception.


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