Web image selection with PLSA

In this paper, we propose a new method to select relevant images to the given keywords from the images gathered from the Web. Our novel method is based on the probabilistic latent semantic analysis (PLSA) model, which is a generative probabilistic topic model. Firstly, we gather images related to the given keywords from the Web with Web search engines. Secondly, we choose pseudo-training images from them by simple heuristic HTML analysis, and train our PLSA-based probabilistic model with them. Finally, we select relevant images from all the gathered images with the learned model. The experimental results shows that the results by the proposed method is almost equivalent to the results by existing methods, although our method does not need to prepare negative training samples in advance unlike existing methods.