Experimental Evaluation of Basic Similarity Measures and their Application in Visual Information Retrieval

Abstract

Searching for similar images is an important feature for image databases and decision support systems in various subject domains. However, it is essential that search results are sorted by degree of similarity in reverse order. This paper presents a comparative analysis of four existing similarity measures and experimentally tests whether they could be used to calculate similarity between images. Metrics could be evaluated by comparing their results to the cumulative human perception of similarity between the same images, obtained by real people. However, this introduces a lot of subjectivism due to nonuniform judgement and evaluation scales. The paper presents a more objective approach -checks which measure performs best in retrieving more images, containing objects of the same type. Results show all four measures could be used to calculate similarity between images, but Jaccard's index performs best in most cases, because it compares features vectors positionally and thus indirectly consider shape, position, orientation and other features.