Two-Stage Texture Image Retrieval by a Combination of Semantic Feature and Visual Feature

By integrating the fuzzy semantic concept and fine visual features, we put forward a two-stage image retrieval (TSIR) methodology. In the first stage, TSIR quickly searches the whole image base according to fuzzy semantic feature, and obtains a semantic search result that characterizes with a "soft" boundary. In the second stage, TSIR retrieves the more accurate images from the semantic search result rather than from the whole collection according to the visual feature. TSIR possesses both the advantages of content-based image retrieval and semantic-based image retrieval. Users of TSIR can quickly browse and search the whole image base with semantic concept. And moreover, they can finely retrieve the more accurate images according to the visual feature of query example. Furthermore, being characterized with the coarse-to-fine strategy, TSIR achieves the improvement of retrieval performance. The experiments based on Brodatz image set show that the retrieval performance of TSIR outperforms the traditional content-based image retrieval system.