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models for categories specified simply by their names. We show that multiple-instance learning enables the recovery of robust category models from images returned by keyword-based search engines. By incorporating constraints that reflect the expected sparsity of true positive examples into a large-margin objective function
Nowadays online image search become more essential. In this paper, we have extended existing system for image re-ranking is explained. The existing system is divided into offline and online parts. In offline part various semantic spaces are automatically learns for different query keywords. Image Semantic content as
We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images
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
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