An image indexing and searching system based both on keyword and content

Abstract

Content-based image retrieval (CBIR) has certain advantages over those pure keyword-based. CBIR indexes images by visual features that are extracted from the images. This may save the effort spent on the manual annotation. However, because low-level visual features, such as colour and texture, often carry no high-level concepts, images retrieved purely based on content may not match with the intention of the user. The work presented in this paper is an image retrieval system that bases both on text annotations and visual contents. It indexes and retrieves images by both keywords and visual features, with the purpose that the keywords may mend the gap between the semantic meaning an image carries and its visual content. Tests were made on the system that have demonstrated that such a hybrid approach did improve retrieval precisions over those pure content-based. © 2008 Springer-Verlag Berlin Heidelberg.