Manifold-Based Combination of Visual Features and Keyword Features for Image Retrieval

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 keyword space graph (KSG) are first constructed by means of manifold-based algorithm to explore the submanifold of Visual space and keyword space. Then, semantic space graph (SSG) is constructed by using neighbor information of KSG and VSF. Afterwards, the VSG and KSG are modified and unified by propagating semantic matrix. Finally, we propose a flexible ranking formula and introduce different relevance feedback methods of different query styles. Experimental results on COREL 5000 images show our method improves image retrieval performance from all aspects.