Fuzzy Aggregation Operators in region-based image retrieval

We examine the effect of the fuzzy aggregation operators on the image retrieval performance, by empirically comparing 67 operators, applied to the problem of computing the image similarity, given a collection of feature similarities of the image regions. While majority of the existing image similarity models express the image similarity as an aggregation of feature similarities, no study presents a systematic comparison of the different operators. We compare the 67 operators by: (1) incorporating each operator into a hierarchical, region-based similarity model, which expresses the image similarity as an aggregation of region similarities, and each region similarity as an aggregation of the corresponding feature similarities; and (2) evaluating the obtained model(s) on five test databases, containing 64,339 general-purpose images, in 749 semantic categories. Results show that the retrieval performance strongly depends on the operator(s) incorporated in the similarity model - the difference in the average retrieval precision between the best and the worst performing of the 67 operators is up to 50%.