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2017 | OriginalPaper | Chapter

Visual Saliency Fusion Based Multi-feature for Semantic Image Retrieval

Authors : Jianan Chen, Cong Bai, Ling Huang, Zhi Liu, Shengyong Chen

Published in: Computer Vision

Publisher: Springer Singapore

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Abstract

In this paper, a saliency fusion based content-based image retrieval method is proposed. Different saliency detection methods were conducted firstly and the output saliency maps were fused by double low rank matrix recovery method. Then the images were segmented into foreground and background according to the fusion result. As the foreground and background had the different impacts on the semantic understanding of the image, different features represented in the form of histogram were extracted. Finally, a fusion of z-score normalized Chi-Square distance is adopted as the similarity measurement. This proposal has been implemented on three widely used benchmark databases and the results evaluated in terms of mean Average Precision (mAP), precision, recall, and F1-measure show that our proposal outperforms the referred state-of-the-art approaches.

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previous chapter Hierarchical Hashing for Image Retrieval
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Metadata
Title
Visual Saliency Fusion Based Multi-feature for Semantic Image Retrieval
Authors
Jianan Chen
Cong Bai
Ling Huang
Zhi Liu
Shengyong Chen
Copyright Year
2017
Publisher
Springer Singapore
Book
Computer Vision

Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-7302-1_11

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