Semantic Image Annotation based on Robust Probabilistic Latent Semantic Analysis
نویسنده
چکیده
Automatic image annotation is a promising solution to enable the semantic image retrieval via keywords. In this paper, we present a robust probabilistic latent semantic analysis (PLSA) for the task of automatic image annotation. On the one hand, since labeled images are often hard to obtain or create in large quantities while the unlabeled ones are easier to collect. Semi-supervised learning aims to achieve good classification performance with the help of unlabelled data in the presence of the small sample size problem. Based on this recognition, the transductive support vector machine (TSVM) is exploited to enhance the quality of the training image data. On the other hand, the traditional bag-of-visual-words model is improved by integrating the contextual semantic information among visual words based on the PLSA. In the meanwhile, the approximation strategy of pseudo-likelihood in Markov random field (MRF) is introduced to combine the feature appearance similarity in feature domain and the contextual semantic information in spatial domain. Extensive experiments on the general-purpose Corel5k dataset validate that the proposed method is much more effective than several state-ofthe-art approaches regarding their effectiveness and efficiency in the tasks of automatic image annotation and retrieval.
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تاریخ انتشار 2016