A Unified Metric Learning-Based Framework for Co-Saliency Detection
A Unified Metric Learning-Based Framework for Co-Saliency Detection
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DOI：
10.1109/tcsvt.2017.2706264
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发表时间：
2018-10
影响因子：
8.4
通讯作者：
Junwei Han;Gong Cheng;Zhenpeng Li;Dingwen Zhang
中科院分区：
文献类型：
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作者：
Junwei Han;Gong Cheng;Zhenpeng Li;Dingwen Zhang
共显著性检测是从一组相关图像中提取出共同显著的目标，由于其广泛的应用而引起了人们的研究兴趣。在实践中，组中的相关图像可能具有大范围的变化，并且显著对象也可能具有大的外观变化。这种广泛的变化通常会带来大的内部共同显着对象（内CO）的多样性和CO与背景之间的高相似性，这使得共同显着性检测任务更加困难。为了解决这些问题，我们最早的努力，引入度量学习的共显着性检测。具体来说，我们提出了一个统一的基于度量学习的框架，共同学习判别特征表示和共显对象检测器。这是通过优化一个新的目标函数来实现的，该目标函数显式地将度量学习正则化项嵌入到支持向量机（SVM）训练中。在这里，度量学习正则化项用于学习具有小的CO内散布的强大特征表示，但是背景和CO之间的大分离，并且SVM分类器用于后续的共显着性检测。在实验中，我们综合评估了两个常用的基准数据集上的方法。与现有的共显著性检测方法相比，达到了最先进的结果。
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.