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

• Published in: IEEE transactions on circuits and systems for video technology Vol. 28; no. 10; pp. 2473 - 2483
• Main Authors: Han, Junwei, Cheng, Gong, Li, Zhenpeng, Zhang, Dingwen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Co-saliency detection; Detectors; Feature extraction; feature learning; Learning; Learning systems; Measurement; metric learning; Object recognition; Regularization; Representations; Salience; State of the art; Support vector machines; Training
• DOI: 10.1109/TCSVT.2017.2706264
• ISSN: 1051-8215

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.