Difference between revisions of "scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines"

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= Introduction =
 
= Introduction =
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This paper<ref>
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Farabet, Clement, et al. [http://arxiv.org/pdf/1202.2160v2.pdf "Scene parsing with multiscale feature learning, purity trees, and optimal covers."] arXiv preprint arXiv:1202.2160 (2012).
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</ref> presents an approach to full scene labelling (FSL). This is the task of giving a label to each pixel in an image corresponding to which category of object it belongs to. FSL involves solving the problems of detection, segmentation, recognition, and contextual integration simultaneously. One of the main obstacles of FSL is that the information required for labelling a particular pixel could come from very distant pixels as well as their labels. This distance often depends on the particular label as well (e.g. the presence of a wheel might mean there is a vehicle nearby, while an object like the sky or water could span the entire image, and figuring out to which class a particular blue pixel belongs could be challenging).
  
 
= Motivation =
 
= Motivation =

Revision as of 15:58, 16 November 2015

Introduction

This paper<ref> Farabet, Clement, et al. "Scene parsing with multiscale feature learning, purity trees, and optimal covers." arXiv preprint arXiv:1202.2160 (2012). </ref> presents an approach to full scene labelling (FSL). This is the task of giving a label to each pixel in an image corresponding to which category of object it belongs to. FSL involves solving the problems of detection, segmentation, recognition, and contextual integration simultaneously. One of the main obstacles of FSL is that the information required for labelling a particular pixel could come from very distant pixels as well as their labels. This distance often depends on the particular label as well (e.g. the presence of a wheel might mean there is a vehicle nearby, while an object like the sky or water could span the entire image, and figuring out to which class a particular blue pixel belongs could be challenging).

Motivation

Model

Results

SceneResultTableStanford.png

SceneResultTableSIFT.png

SceneResultTableBarcelona.png

SceneResultPictures.png

References

<references />