Rapid Relevance Feedback Strategy Based on Distributed CBIR System

Jianxin Liao, Baoran Li, Jingyu Wang, Qi Qi, Jing Wang and Tonghong Li
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Jianxin Liao: Beijing University of Posts and Telecommunications, Beijing, China
Baoran Li: Beijing University of Posts and Telecommunications, Beijing, China
Jingyu Wang: Beijing University of Posts and Telecommunications, Beijing, China
Qi Qi: Beijing University of Posts and Telecommunications, Beijing, China
Jing Wang: Beijing University of Posts and Telecommunications, Beijing, China
Tonghong Li: Technical University of Madrid, Madrid, Spain

International Journal on Semantic Web and Information Systems (IJSWIS), 2018, vol. 14, issue 2, 1-26

Abstract: This article describes the capability of online data storage which has been enhanced by the emergence of cloud datacenter development. Distributed Hash Table (DHT) based image retrieval system using locality sensitive hash (LSH) has provided an efficient way to set up distributed Content Based Image Retrieval (CBIR) frameworks. However, with the fixed LSH function adopted, LSH and other codebook-based distributed retrieval systems are facing the problem of flexibility, and also are difficult to satisfy the user's demand. In this article, LRFMIR is proposed to introduce semantic search into DHT based CBIR system. LRFMIR is established on a DHT based network, where a flexible result truncating strategy is employed to fuse provided results by using multiple features measurements. Experiments show that LRFMIR provides a higher accuracy and recall rate than single feature employed retrieval systems, and possesses good load balancing and query efficiency performance.

Date: 2018
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