An efficient semantic – Related image retrieval method

• Published in: Expert systems with applications Vol. 72; pp. 30 - 41
• Main Authors: Dao Thi Thuy, Quynh, Nguyen Huu, Quynh, Phuong Van, Canh, Ngo Quoc, Tao
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.04.2017; Elsevier BV
• Subjects: Content-based image retrieval; Feedback; Image management; Image retrieval; Importance of feature; Semantic weight; Semantic-related image; Semantics; Studies; User feedback; Importance of feature; Semantic weight; Content-based image retrieval; Semantic-related image
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2016.12.004

•Retrieving semantic images spread out in the entire feature space.•Determining the semantic weight of each query in combined distance calculation.•Identifying the importance for each feature.•Our method is quite effective, improving the retrieval in one feedback iteration. Many previous techniques were designed to retrieve semantic images in a certain neighborhood of the query image and thus bypassing the semantically related images in the whole feature space. Several recently methods were designed to retrieve semantically related images in the entire feature space but with low precision. In this paper, we propose a Semantic – Related Image Retrieval method (SRIR), which can retrieve semantic images spread in the entire feature space with high precision. Our method takes advantage of the user feedback to determine the semantic importance of each query and the importance of each feature. In addition, the retrieval time of our method does not increase with the number of user feedback. We also provide experimental results to demonstrate the effectiveness of our method.