A new image retrieval system supporting query by semantics and example

In this paper, we propose a new image retrieval system that provides users with both semantics based query and visual features based query. Our system has several advantages. First, it integrates visual features and semantics seamlessly. Second, it uses some effective techniques such as image classification, relevance feedback to bridge the gap between visual features and semantics. Third, it proposes several ways to obtain the semantic information of the image, which reduces manual labor and reduces the "subjectivity" of semantics by human. Fourth, it can update semantics of the image by human's intervention, which makes the image retrieval more flexible. We have implemented an image retrieval system based on our proposed image retrieval approach. Experiments on an image database containing 22000 show that our scheme can achieve high efficiency.