An appropriate similarity measure between images is one of the key techniques in search-based image annotation models. In order to capture the nonlinear relationships between visual features and image semantics, many kernel distance metric learning(KML) algorithms have been developed. However, when challenged with large-scale image annotation, their metrics can't explicitly represent the similarity between image semantics, and their algorithms suffer from high computation cost. Therefore, they always lose their efficiency. In this paper, we propose a manifold kernel metric learning (M_KML) algorithm. Our M_KML algorithm will simultaneously learn the manifold structure and the image annotation metrics. The main merit of our M_KML algorithm is that the distance metrics are builded on image feature's interior manifold structure, and the dimensionality reduction on manifold structure can handle the high dimensionality challenge faced by KML. Final experiments verify our method's efficiency and effectiveness by comparing it with state-of-the-art image annotation approaches.
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