Learning to Detect A Salient Object

Tie Liu, Zejian Yuan, Jian Sun, Jingdong Wang, N. Zheng, Xiaoou Tang, H. Shum
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引用次数: 2578

Abstract

We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, center-surround histogram, and color spatial distribution to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. We also constructed a large image database containing tens of thousands of carefully labeled images by multiple users. To our knowledge, it is the first large image database for quantitative evaluation of visual attention algorithms. We validate our approach on this image database, which is public available with this paper.
学习发现一个突出的物体
我们通过检测输入图像中的显著物体来研究视觉注意。我们将显著目标检测作为图像分割问题,将显著目标从图像背景中分离出来。我们提出了一套新的特征,包括多尺度对比度、中心环绕直方图和颜色空间分布来描述局部、区域和全局的显著目标。学习一个条件随机场来有效地结合这些特征进行显著目标检测。我们还构建了一个大型图像数据库,其中包含成千上万张由多个用户精心标记的图像。据我们所知,这是第一个用于视觉注意算法定量评估的大型图像数据库。我们在此图像数据库上验证了我们的方法,该数据库与本文一起公开提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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