Object recognition and segmentation using SIFT and Graph Cuts
Object recognition and segmentation using SIFT and Graph Cuts
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DOI：
10.1109/icpr.2008.4761400
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Akira Suga;K. Fukuda;T. Takiguchi;Y. Ariki
中科院分区：
文献类型：
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作者：
Akira Suga;K. Fukuda;T. Takiguchi;Y. Ariki
提出了一种基于尺度不变特征变换(SIFT)和图割的目标识别与分割方法。SIFT特征对于旋转、比例变化和光照变化都是不变的，经常用于目标识别。然而，在以往的基于SIFT的目标识别工作中，简单地通过仿射变换来假设目标区域，而没有对准确的目标区域进行分割。另一方面，提出了一种基于图割的细节目标区域分割方法。但人工播种是必要的。该方法将SIFT和图割相结合，首先通过对SIFT关键点的投票处理来识别物体的存在。之后，使用SIFT关键点作为种子，通过图切切出对象区域。由于这种组合，识别和分割都是在杂乱的背景下自动执行的，包括遮挡。
In this paper, we propose a method of object recognition and segmentation using scale-invariant feature transform (SIFT) and graph cuts. SIFT feature is invariant for rotations, scale changes, and illumination changes and it is often used for object recognition. However, in previous object recognition work using SIFT, the object region is simply presumed by the affine-transformation and the accurate object region was not segmented. On the other hand, graph cuts is proposed as a segmentation method of a detail object region. But it was necessary to give seeds manually. By combing SIFT and graph cuts, in our method, the existence of objects is recognized first by vote processing of SIFT keypoints. After that, the object region is cut out by graph cuts using SIFT keypoints as seeds. Thanks to this combination, both recognition and segmentation are performed automatically under cluttered backgrounds including occlusion.