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LearningtoLocalizeObjectswithStructuredOutputRegre.ppt

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LearningtoLocalizeObjectswithStructuredOutputRegre

Learning to Localize Objectswith Structured Output Regression 讲解人: 郑伟提纲作者信息相关文章摘要内容问题和动机文章的内容 SVMs SVMs in Structured Space SVMs in Detection Task Branch-and-Bound Search 实验结果结论和总结作者信息(1/3) 作者单位 Kaiser Wilhelm Gesellschaft (1911) Albert Einstein Max Planck (president, 1930~1937, 1945~1946) Max Planck Society (after World War II, 1948) Max Planck Institute for Biological Cybernetics 作者信息(2/3) 第一作者 Matthew?Blaschko Graduate Student? “I am interested in the interface between machine learning and computer vision. ” The best student paper prize at ECCV 2008 The best paper prize at CVPR 2008 The DAGM2008 Main Prize 作者信息(3/3) 第二作者 Christoph?Lampert,?Dr. Research Scientist “I joined the Computer Vision Lab as local coordinator of the EU project CLASS in 2007.” Multimodal Computer Vision Unsupervised and Semi-Supervised Learning Complex Methods on Machine Learning 提纲作者信息相关文章摘要内容问题和动机文章的内容 SVMs SVMs in Structured Space SVMs in Detection Task Branch-and-Bound Search 实验结果结论和总结相关文章 Learning to Localize Objects with Structured Output Regression (ECCV 08’) Beyond Sliding Windows: Object Localization by Efficient Subwindow Search (CVPR 08’) Support Vector Machine Learning for Interdependent and Structured Output Spaces (ICML 04’) SURF: Speeded Up Robust Features（ECCV 06’）提纲作者信息相关文章摘要内容问题和动机文章的内容 SVMs SVMs in Structured Space SVMs in Detection Task Branch-and-Bound Search 实验结果结论和总结摘要内容 Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the pre