假设定及文献研究hypotheses development and literature search.pdf下载

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假设定及文献研究hypotheses development and literature search , 相关下载链接：https://download.csdn.net/download/sinat_22753035/73393693?utm_source=bbsseo

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假设定及文献研究hypotheses development and literature search

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Machine Learning by Tom M. Mitchell (1997-03-01) https://www.amazon.com/gp/product/B01FIYYP6W?pf_rd_p=c2945051-950f-485c-b4df-15aac5223b10&pf_rd_r=7WB8PHPHB10Z2C4A7H64

机器学习基石5 - 3 - Effective Number of Hypotheses (16-17).mp4

作者：Marc Bosch,Christopher M. Gifford,Austin G. Dress,Clare W. Lau,Jeffrey G. Skibo,Gordon A. Christie 摘要：Image segmentation is an important component of many image understanding systems. It aims to group pixels in a spatially and perceptually coherent manner. Typically, these algorithms have a collection of parameters that control the degree of over-segmentation produced. It still remains a challenge to properly select such parameters for human-like perceptual grouping. In this work, we exploit the diversity of segments produced by different choices of parameters. We scan the segmentation parameter space and generate a collection of image segmentation hypotheses (from highly over-segmented to under-segmented). These are fed into a cost minimization framework that produces the final segmentation by selecting segments that: (1) better describe the natural contours of the image, and (2) are more stable and persistent among all the segmentation hypotheses. We compare our algorithm's performance with state-of-the-art algorithms, showing that we can achieve improved results. We also show that our framework is robust to the choice of segmentation kernel that produces the initial set of hypotheses.

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