Paper

A Unified Metric Learning-Based Framework for Co-Saliency Detection

Volume Number:
28
Issue Number:
10
Pages:
2473
–
2483
Publication Date:
May 2017

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Abstract

Co-saliency detection, which focuses on extracting commonly salient objects in a group of relevant images, has been attracting research interest because of its broad applications. In practice, the relevant images in a group may have a wide range of variations, and the salient objects may also have large appearance changes. Such wide variations usually bring about large intra-co-salient objects (intra-COs) diversity and high similarity between COs and background, which makes the co-saliency detection task more difficult. To address these problems, we make the earliest effort to introduce metric learning to co-saliency detection. Specifically, we propose a unified metric learning-based framework to jointly learn discriminative feature representation and co-salient object detector. This is achieved by optimizing a new objective function that explicitly embeds a metric learning regularization term into support vector machine (SVM) training. Here, the metric learning regularization term is used to learn a powerful feature representation that has small intra-COs scatter, but big separation between background and COs and the SVM classifier is used for subsequent co-saliency detection. In the experiments, we comprehensively evaluate the proposed method on two commonly used benchmark data sets. The state-of-the-art results are achieved in comparison with the existing co-saliency detection methods.

Description

J. Han, G. Cheng, Z. Li and D. Zhang, "A Unified Metric Learning-Based Framework for Co-Saliency Detection," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 2473-2483, Oct. 2018, doi: 10.1109/TCSVT.2017.2706264.