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This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic image annotation, we define a novel spatial string kernel to quantify the similarity
integrates the discriminative classification with the generative model to mutually complete their advantages. In addition, not only the relevance model between the visual content of images and the textual keywords but also the property of keyword correlation is exploited in the proposed AIA scheme. Particularly, to establish an
Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Different statistical models and machine learning methods have been introduced for image auto-annotation. In this paper, we propose a collaborative approach, in which multiple different statistical models are
semantic analysis (LSA) is employed to the NN based annotation scheme (noted as LSA-NN) for discovering the latent contextual correlation among the keywords, which is neglected by many previous annotation methods. Instead of region-level as most previous works do, the LSA-NN based annotation scheme is built at image-level to
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