Perceived Similarity and Visual Descriptions in Content-Based Image Retrieval

The use of low-level feature descriptors is pervasive in content-based image retrieval tasks and the answer to the question of how well these features describe users´ inten- tion is inconclusive. In this paper we devise experiments to gauge the degree of alignment between the description of target images by humans and that implicitly provided by low-level image feature descriptors. Data was collected on how humans perceive similarity in images. Using images judged by humans to be similar, as ground truth, the per- formance of some MPEG-7 visual feature descriptors were evaluated. It is found that various descriptors play different roles in different queries and their appropriate combination can improve the performance of retrieval tasks. This forms a basis for the development of adaptive weight assignment to features depending on the query and retrieval task.