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首页> 外文期刊>Journal of visual communication & image representation >Surroundedness based multiscale saliency detection
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Surroundedness based multiscale saliency detection

机译：基于环境的多尺度显着性检测

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In this paper, a surroundedness-based multiscale saliency method is proposed based on the Gestalt principles for figure-ground segregation, which states that (1) surrounded regions are more likely to be perceived as figures, (2) the humans understand the external stimuli as whole rather than the sum of their parts. First, an image is characterized by a set of binary images, which is generated by a simple and effective homogeneous region extraction method with well contour preservation. And the contour confidence map is obtained by a fast contour detection method. Then for each connect homogeneous region in a binary map, surroundedness is defined by the average outer contour confidence. Finally, integrating the background priors, multiscale saliency maps are generated and combined to the final saliency map. The proposed method is evaluated on two widely used public datasets with pixel accurate salient region annotations using both precision and recall analysis and ROC analysis. And the experimental results show that the proposed method outperforms 14 alternative methods. (C) 2015 Elsevier Inc. All rights reserved.
机译：在本文中，基于格式塔原则，提出了一种基于包围度的多尺度显着性图地隔离方法，该方法指出：（1）周围区域更容易被视为人物，（2）人类理解外部刺激整体而非部分的总和。首先，图像以一组二进制图像为特征，该二进制图像是通过简单且有效的均质区域提取方法生成的，并具有良好的轮廓保留。并且通过快速轮廓检测方法获得轮廓置信度图。然后，对于二元图中的每个连接均质区域，通过平均外部轮廓置信度定义包围度。最后，整合背景先验，生成多尺度显着图并将其组合到最终显着图。该方法在两个广泛使用的公共数据集上进行了评估，这些数据集使用了精确度和召回率分析以及ROC分析，具有像素精确的显着区域注释。实验结果表明，该方法优于14种方法。 （C）2015 Elsevier Inc.保留所有权利。

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