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Multiple images segmentation based on saliency map

机译:基于显着性图的多图像分割

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摘要

Aiming at discovering and segmenting out common objects from multiple images, co-segmentation is a effective method. It is more accurate to make full use of the relationships between images in segmenting than only single image. The first step is to deal with single image with employing hierarchical segmentation to get a Contour Map, saliency detection to obtain the saliency map and object detection to find the possible common part. Then, constructing a digraph with the multiple local regions, and dealing with the digraph. When a digraph is constructed, the corresponding between adjacent two images is influential to the co-segmentation results. This paper develops a method to sort the images to co-segment. Also, we test the method on ICOSEG and MSRC datasets, and compare it with four proposed method. And the results show that it is efficient in co-segmentation with higher precision than many existing and conventional co-segmentation methods.
机译:为了从多个图像中发现并分割出共同的物体,共分割是一种有效的方法。与仅单个图像相比,在分割中充分利用图像之间的关系更为准确。第一步是使用分层分割处理单个图像以获取轮廓图,通过显着性检测获得显着性图,并通过对象检测来找到可能的公共部分。然后,用多个局部区域构造一个有向图,并处理该有向图。当构造有向图时,相邻两个图像之间的对应关系会影响共同细分结果。本文提出了一种对图像进行分类的方法。此外,我们在ICOSEG和MSRC数据集上测试了该方法,并将其与四种提出的方​​法进行了比较。结果表明,与许多现有和常规的协同细分方法相比,该算法在联合细分中具有很高的效率。

著录项

  • 来源
    《Pattern recognition》|2017年|104430N.1-104430N.5|共5页
  • 会议地点 Singapore(SG)
  • 作者单位

    Information Science and Engineering, Hunan University, 410082 Changsha, China;

    Information Science and Engineering, Hunan University, 410082 Changsha, China;

    Information Science and Engineering, Hunan University, 410082 Changsha, China;

    Information Science and Engineering, Hunan University, 410082 Changsha, China;

    Civil Engineering, Hunan University, 410082 Changsha, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    co-segmentation; saliency map; digraph; sequence;

    机译:共同细分;显着图图序列;
  • 入库时间 2022-08-26 14:06:55

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