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Semi-Supervised Online Multi-Kernel Similarity Learning for Image Retrieval

Liang, J, Hu, Q, Wang, Wenwu and Han, Y (2016) Semi-Supervised Online Multi-Kernel Similarity Learning for Image Retrieval IEEE Transactions on Multimedia, 19 (5). pp. 1077-1089.

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Abstract

Metric learning plays a fundamental role in the fields of multimedia retrieval and pattern recognition. Recently, an online multi-kernel similarity (OMKS) learning method has been presented for content-based image retrieval (CBIR), which was shown to be promising for capturing the intrinsic nonlinear relations within multimodal features from large-scale data. However, the similarity function in this method is learned only from labeled images. In this paper, we present a new framework to exploit unlabeled images and develop a semi-supervised OMKS algorithm. The proposed method is a multi-stage algorithm consisting of feature selection, selective ensemble learning, active sample selection and triplet generation. The novel aspects of our work are the introduction of classification confidence to evaluate the labeling process and select the reliably labeled images to train the metric function, and a method for reliable triplet generation, where a new criterion for sample selection is used to improve the accuracy of label prediction for unlabelled images. Our proposed method offers advantages in challenging scenarios, in particular, for a small set of labeled images with high-dimensional features. Experimental results demonstrate the effectiveness of the proposed method as compared with several baseline methods.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Liang, JUNSPECIFIEDUNSPECIFIED
Hu, QUNSPECIFIEDUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Han, YUNSPECIFIEDUNSPECIFIED
Date : 23 October 2016
Identification Number : 10.1109/TMM.2016.2644862
Copyright Disclaimer : 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords : Image retrieval, metric learning, similarity learning, multi-kernel learning, semi-supervised, OMKS, SSOMKS
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 11 Jan 2017 18:26
Last Modified : 31 Oct 2017 19:03
URI: http://epubs.surrey.ac.uk/id/eprint/813271

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