Performance evaluation of Euclidean/correlation-based relevance feedback algorithms in content-based image retrieval systems

In this paper, we evaluate and investigate two main types of relevance feedback algorithms; the Euclidean and the correlation-based approaches. In the first case, we examine heuristic and optimal techniques, which exploit either on the weighted or the generalized Euclidean distance. In the second type, two different ways for parametrizing the cross-correlation similarity metric are proposed. The first scales only the elements of the query feature vector, while the second scales both the query and the selected samples. All the examined algorithms are evaluated using objective criteria, such as the precision-recall curve and the average normalized modified retrieval rank (ANMRR). Discussions and comparisons of all the aforementioned relevance feedback algorithms are presented.