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Reduce Semantic Gap in Content-Based Image Retrieval

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CBIR is an image retrieval system that allows user to send query image to the system then gives all similar images based on content of the query image. In CBIR system searches and finds digital images that exist in the database based on the analysis of image features. However, CBIR still have problems in semantic gap and computational load (viz. memory consumed and execution time). There are some techniques of Relevance Feedback in CBIR that have been proposed including ULBRF, FA-RF, NPRF, and PSORF. Based on the results of literature study showed that the Relevance Feedback techniques can reduce semantic gap. However, theses techniques are still left several problems viz. the high computation load and amount of feedback required. Therefore, in order to address of the problems, advised a method namely user feedback objects composite that based on the selection of feedback objects that contained in the image through user feedback’s. This method is supposed to reduce semantic gap, improve precision and recall, and decrease the number of execution times.
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Keywords: CBIR; Feedback Objects; Relevance Feedback; Semantic Gap

Document Type: Research Article

Affiliations: School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia

Publication date: November 1, 2017

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