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Similarity measures for efficient content-based image retrieval

Similarity measures for efficient content-based image retrieval

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New similarity measures for comparing two colour histograms are described: the dissimilitude distance DS* and the similarity distance E. The latter is incorporated into the exponentiation part of the Gibbs distribution and the generalised Dirichlet mixture, while the former is compared to five similarity measures: L1, L2 (Euclidean distance), the similarity measure E in addition to Gibbs and Dirichlet distributions integrating E. The proposed measures are implemented into a system called MIRA for an efficient content-based image mining and retrieval. In order to overcome the limitations (and inappropriateness) of some previous information retrieval measures in evaluating the efficiency of an image retrieval process, three variants of a new effectiveness measure are proposed and experimented on an image collection for various similarity measures, including L1 and L2. Experimental results show that retrieval effectiveness is the highest for E + Dirichlet and the lowest for the Euclidean distance. They also illustrate the superiority of our approach towards similarity analysis and retrieval effectiveness computation both in the L* C* H* and CIECAM02 colour spaces.

Inspec keywords: content-based retrieval; image colour analysis; image retrieval

Other keywords: Dirichlet mixture; Gibbs distribution; content-based image mining; Euclidean distance; colour histogram; content-based image retrieval

Subjects: Optical, image and video signal processing; Information retrieval techniques; Computer vision and image processing techniques

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