Is visual saliency useful for content-based image retrieval?

Is visual saliency useful for content-based image retrieval? In the real world, people often focus on the distinctive objects (Salient Regions, SR) in a scene. Thus, a number of saliency detection methods are introduced into content-based image retrieval (CBIR), which is often with Bag of Words (BoW) model. These methods aim to use the saliency map to prune keypoints or discard the keypoints from the background. However, these methods do not consider the background of the image and the characteristics of the dataset itself. In this paper we focus on the following two issues: 1) whether the saliency pruning method is useful for image retrieval in different kinds of datasets (e.g., salient/cluttered, mixed image database); 2) we test the effectiveness of the discarded parts from the background (Non-Salient Regions, Non-SR) for different kinds of image database. In order to demonstrate the performance of using visual saliency, we conduct experiments on two publicly available database (Ukbench, Holidays). The experiments reveal that the way of using saliency map to filter a small amount of key-points can clearly improve the performance of CBIR, and the keypoints in the background are also useful in some kinds of image datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Is visual saliency useful for content-based image retrieval?

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-5001-6
Publisher site
See Article on Publisher Site

Abstract

In the real world, people often focus on the distinctive objects (Salient Regions, SR) in a scene. Thus, a number of saliency detection methods are introduced into content-based image retrieval (CBIR), which is often with Bag of Words (BoW) model. These methods aim to use the saliency map to prune keypoints or discard the keypoints from the background. However, these methods do not consider the background of the image and the characteristics of the dataset itself. In this paper we focus on the following two issues: 1) whether the saliency pruning method is useful for image retrieval in different kinds of datasets (e.g., salient/cluttered, mixed image database); 2) we test the effectiveness of the discarded parts from the background (Non-Salient Regions, Non-SR) for different kinds of image database. In order to demonstrate the performance of using visual saliency, we conduct experiments on two publicly available database (Ukbench, Holidays). The experiments reveal that the way of using saliency map to filter a small amount of key-points can clearly improve the performance of CBIR, and the keypoints in the background are also useful in some kinds of image datasets.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 31, 2017

References

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