Detection of multiple salient objects through the integration of estimated foreground clues
Detection of multiple salient objects through the integration of estimated foreground clues
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DOI：
10.1016/j.imavis.2016.07.007
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
K. Oh;Myungeun Lee;G. Kim;Soohyung Kim
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文献类型：
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作者：
K. Oh;Myungeun Lee;G. Kim;Soohyung Kim
本文提出了一种基于估计前景线索融合的多显著区域检测方法。尽管对于显著对象的检测这一主题已经进行了很好的研究，但是关于多对象检测任务仍然存在许多技术挑战；尤其是，与单对象检测问题不同，对象间的高差异性导致了新的困难。通过分析现有模型的局限性，提出了基于多层次前景分割策略的两个主要框架：基于非参数簇的显著度(NS)和基于参数簇的显著度(PS)。每个框架包括向量分类、前景估计、能量生成和整合过程。与以往的模型相比，该方法不依赖于对比度特征，也不受物体的大小、厚度和形状的影响。实验结果表明，该方法对SED2基准测试达到了较高的检测准确率，相应的准确率和召回率均优于现有方法，在MSRA-ASD、SED2和CSSD基准测试中也取得了更好的性能。
In this paper, a novel method for the detection of multiple salient regions that is based on the integration of estimated foreground clues is proposed. Although this subject has been very well studied for the detection of salient objects, many technical challenges still exist regarding the multiple-object-detection task; in particular, unlike a single-object-detection problem, a high inter-object dissimilarity causes new difficulties. By analyzing the limitations of the existing models, the following two main frameworks that are based on a multi-level foreground-segmentation strategy are proposed: non-parametric cluster-based saliency (NS) and parametric cluster-based saliency (PS). Each framework consists of a vector classification, a foreground estimation, an energy generation, and an integration process. In contrast to previous models, the proposed method is not dependent upon the contrast features, and is unaffected by the size, thickness, and shape of the objects. In the experiment results, a superior detection accuracy for the SED2 benchmark was achieved with the use of the proposed scheme; furthermore, the corresponding precision and recall are superior to those of the state-of-the-art approaches, and more effective performances were also achieved on the MSRA-ASD, SED2 and CSSD benchmarks.