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Rapid Relevance Feedback Strategy Based on Distributed CBIR System

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jianxin Liao & baoran li & Jingyu Wang & Qi Qi & Jing Wang & Tonghong Li, 2018. "Rapid Relevance Feedback Strategy Based on Distributed CBIR System," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(2), pages 1-26, April.
    • Handle: RePEc:igg:jswis0:v:14:y:2018:i:2:p:1-26
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