Improved Image Retrieval Using Automatic Image Sorting and Semi-automatic Generation of Image Semantics

In this paper we propose a new image search system using keyword annotations and low-level visual meta-data to generate inter-image relationships. Unlike other approaches the new system does not try to learn the degree of confidence between images and associated keywords. We rather propose to model the degree of similarity between images by building up a network of linked images. The weights of the inter-image links are learned from the userspsila interaction with the system only. For each image search a set of candidate images is selected from a visually sorted arrangement of result images. This candidate set is used to refine the result by filtering out non-suiting images from a larger set of further result images. Semantic inter-image relation-ships of images can be modeled by collecting the candidate sets from many searches. Our system improves Internet image search significantly.