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To bridge the semantic gap between low-level visual features and high-level semantic concepts, this paper puts forward a novel semi-supervised learning framework of combining visual and keyword features. We assume all of images in the database have been annotated. In this framework, the visual space graph (VSG) and
The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs. Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper
The relevance feedback techniques have been studied in the field of document retrieval, aiming to generate appropriate queries for userspsila information needs.Conventional relevance feedback techniques are performed on document space, while the resultant queries should be represented in keyword space. In this paper
The associations between different modalities of Web images could be very useful for Web image retrieval. In this paper, we investigate the multi-modal associations between two basic modalities of Web images, i.e. keyword and visual feature clusters, by data mining technique. The association rule crosses two
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
region of initial retrieved results. Both keywords and image contents of the Web images are computed by LLSI to re-rank the initial retrieval results automatically. The PRF-LLSI contribute to the following: (1) Local LSI resolves the heavy computation cost of LSI; (2) Pseudo Relevance Feedback doesn't need the user's
In our earlier works on VAST (visuAl & semantic image search) system, the semantic network effectively associated keywords and visual feature clusters. However, we only concerned about the construction of the semantic network before, and did not consider the updating of the semantic network. In this paper, an
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