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Image classification using latent spatial pyramid matching

Resource type
Thesis type
((Thesis)) M.Sc.
Date created
2011-08-23
Authors/Contributors
Author: Yu, Pengfei
Abstract
We present work on image classification in this thesis. Image classification is a classical task in computer vision, whose goal is to determine whether or not any instances of a particular object class appear in a given image. There are three major pieces of work. First, we proposed a novel Latent Spatial Pyramid Matching (L-SPM) feature representation inspired by the state-of-art Spatial Pyramid Matching (SPM) [29] feature representation. L-SPM allows the cells of the pyramid to move within reasonable regions instead of a predefined rigid partition. Second, we utilize Efficient Subwindow Search [28] based on a branch-and-bound algorithm to select the position and size for the latent cells. Third, we implement the Latent SVM framework proposed by Felzenszwalb et al. [21] to solve the non-convex optimization problem. Results are reported for image classification on the Pascal VOC 2007 data set.
Document
Identifier
etd6833
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Copyright is held by the author.
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The author granted permission for the file to be printed and for the text to be copied and pasted.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Mori, Greg
Member of collection
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etd6833_PYu.pdf 14.9 MB

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