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Improving Image Retrieval by Local Feature Reselection with Query Expansion

  • Hanli WangEmail author
  • Tianyao Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

A novel approach related to query expansion is proposed to improve image retrieval performance. The proposed approach investigates the problem that not all of the visual features extracted from images are appropriate to be employed for similarity matching. To address this issue, we distinguish image features as effective features from noisy features. The former is benefit for image retrieval while the latter causes deterioration, since the matching of noisy features may rise the similarity score of irrelevant images. In this work, a detailed illustration of effective and noisy features is given and the aforementioned problem is solved by selecting effective features to enhance query feature set while removing noisy features via spatial verification. Experimental results demonstrate that the proposed approach outperforms a number of state-of-the-art query expansion approaches.

Keywords

Image retrieval Query expansion Feature reselection 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyTongji UniversityShanghaiChina
  2. 2.Key Laboratory of Embedded System and Service Computing, Ministry of EducationTongji UniversityShanghaiChina

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