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designed and implemented to resolve the problem of crossing language queries and retrieving images processes. It can greatly reduce lot of time and effort for the search. The experiments on diverse queries on Yahoo images search have shown that the proposed scheme can improve the images results for non-English keyword
This paper presents a case study of an image retrieval system based on a notion of similarity between images in a multimedia database and where a user request can be an image file or a keyword. The CBIR (content based image retrieval) system, the current system of search for information (SSI) -e.g. PEIR, MIRC, MIR
natural language use. Even though word sense and concept extraction is major challenge which comes up with keywords. Information can be presented in better way with image presentation, which is been used in news portals to communicate fastly happing news and social websites instagram Facebook, flicker .user purchase goods by
appear on websites with other text contents which can deliver important information about the image semantics. Popular image search engines use text contents surrounding the image to generate annotation keywords. Also emphasized text contents like headlines are assumed to be important description providers. Otherwise we
Information Retrieval Systems and Search engines lack capability to Map Human perception, as words have limited expression power come up with ambiguity in different contexts and concepts. A picture or image is bigger broader and best way to express thing. An image is concept that represents information urge in more
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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