Efficient content-based image retrieval based on multi-feature fusion
Nanayakkara Wasam Uluwitige, Dinesha Chathurani, Geva, Shlomo, Chappell, Timothy, & Chandran, Vinod (2016) Efficient content-based image retrieval based on multi-feature fusion. In 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), 17-21 July 2016, Pisa, Italy.
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The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.
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|Item Type:||Conference Paper|
|Keywords:||CBIR, Content based image retrieval, Pseudo relevance feedback, Image signature|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2016 ACM|
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|Deposited On:||05 May 2016 22:48|
|Last Modified:||09 Aug 2016 05:49|
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