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用于对象分类和检测的全局和高效自相似性
Global and Efficient Self-Similarity for Object Classification and Detection |
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| 课程网址: |
http://videolectures.net/cvpr2010_deselaers_gess/
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| 主讲教师: |
Thomas Deselaers
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| 开课单位: |
苏黎世联邦理工学院
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| 开课时间: |
2010-07-19
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| 课程语种: |
英语
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| 中文简介: |
自相似性是一种极具吸引力的图像属性，它最近以局部自相似性描述符的形式进入对象识别[5,6,14,18,23,27]。在本文中，我们探索全局自相似性（GSS）及其在本地自我中的优势相似度（LSS）。我们做出三个贡献：（a）我们提出计算有效的算法来提取GSS描述符进行分类。这些捕获了整个图像中自相似性的空间排列; （b）我们展示了如何在滑动窗口框架和分支定界框架中有效地使用这些描述进行检测; （c）我们通过实验证明了Pascal VOC 2007和ETHZ ShapeClasses，GSS在分类和检测方面优于LSS，并且GSS描述符与传统描述符（例如梯度或颜色）互补。
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| 课程简介: |
Self-similarity is an attractive image property which has recently found its way into object recognition in the form of local self-similarity descriptors [5, 6, 14, 18, 23, 27] In this paper we explore global self-similarity (GSS) and its advantages over local self-similarity (LSS). We make three contributions: (a) we propose computationally efficient algorithms to extract GSS descriptors for classification. These capture the spatial arrangements of self-similarities within the entire image; (b) we show how to use these descriptors efficiently for detection in a sliding-window framework and in a branch-and-bound framework; (c) we experimentally demonstrate on Pascal VOC 2007 and on ETHZ Shape Classes that GSS outperforms LSS for both classification and detection, and that GSS descriptors are complementary to conventional descriptors such as gradients or color.
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| 关 键 词: |
自相似性; 图像属性; 滑动窗口框架
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-07-16：yumf
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| 阅读次数: |
67
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