Abstract
In recent years, Bag-of-Visual-Word (BoVW) model has been widely used in computer vision. However, BoVW ignores not only spatial information but also semantic information between visual words. In this study, a latent Dirichlet allocation (LDA) based model has been proposed to obtain the semantic relations of visual words. Because the LDA-based topic model used alone usually degrade performance. Thus, a visual language model (VLM) is combined with LDA-based topic model linearly to represent each image. On our dataset, the proposed approach has been compared with state-of-the-art approaches (such as BoVW, LLC, SPM and VLM). Experimental results indicate that the proposed approach outperforms the original BoVW, LLC, SPM and VLM.
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