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Distance metric learning for image and webpage comparison

Marc Teva Law 1 
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This thesis focuses on distance metric learning for image and webpage comparison. Distance metrics are used in many machine learning and computer vision contexts such as k-nearest neighbors classification, clustering, support vector machine, information/image retrieval, visualization etc. In this thesis, we focus on Mahalanobis-like distance metric learning where the learned model is parametered by a symmetric positive semidefinite matrix. It learns a linear tranformation such that the Euclidean distance in the induced projected space satisfies learning constraints.First, we propose a method based on comparison between relative distances that takes rich relations between data into account, and exploits similarities between quadruplets of examples. We apply this method on relative attributes and hierarchical image classification. Second, we propose a new regularization method that controls the rank of the learned matrix, limiting the number of independent parameters and overfitting. We show the interest of our method on synthetic and real-world recognition datasets. Eventually, we propose a novel Webpage change detection framework in a context of archiving. For this purpose, we use temporal distance relations between different versions of a same Webpage. The metric learned in a totally unsupervised way detects important regions and ignores unimportant content such as menus and advertisements. We show the interest of our method on different Websites.
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Submitted on : Wednesday, March 18, 2015 - 3:07:05 PM
Last modification on : Saturday, July 9, 2022 - 3:27:23 AM
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  • HAL Id : tel-01135698, version 2


Marc Teva Law. Distance metric learning for image and webpage comparison. Other [cs.OH]. Université Pierre et Marie Curie - Paris VI, 2015. English. ⟨NNT : 2015PA066019⟩. ⟨tel-01135698v2⟩



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