ENHANCEMENT OF QUERY WITH RELEVANCE FEEDBACK USING GENETIC ALGORITHMS IN COMBINATION WITH NEIGHBORHUTSURT METHODS FOR CONTENT-BASED IMAGES RETRIEVAL.

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Suttilug Choonprawat
Songpon Nakharacruangsak

Abstract

Currently, the publishing of images on the Internet is growing rapidly. This is a consequence


of the advancement of the tool. and technology. resulting in a large number of published images. To search for the desired image. Requires a highly efficient and capable image retrieval system. The old retrieval format used keywords from metadata. by comparing it with the question. But what if two users want to retrieve the same image? In which each user does not use the same question text. Make the system retrieve images from text. difficult to consider and compare. Improving questions with feedback correlation using genetic algorithms in combination with neighboring hoots art methods. It is a semi-automatic system. User can select the correct result image according to their requirement from past retrieval cycle. and make improvements to a new question image. and feed back into the system As a result, the resulting image is more suitable for the user's needs. The results of the experiment showed that this method by the researchers gave an average F-measure of 0.87. When, which means the system is performing at a good level and yielded higher results than the results of the first round of image retrieval without the reverse correlation feed process. which meets the stated objectives of the research

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Choonprawat ส., & Nakharacruangsak ท. (2022). ENHANCEMENT OF QUERY WITH RELEVANCE FEEDBACK USING GENETIC ALGORITHMS IN COMBINATION WITH NEIGHBORHUTSURT METHODS FOR CONTENT-BASED IMAGES RETRIEVAL. Journal of Energy and Environment Technology of Graduate School Siam Technology College, 9(1), 92–102. Retrieved from https://ph01.tci-thaijo.org/index.php/JEET/article/view/248598
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Research Article

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