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

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(Introduction)
(Motivation)
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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).
 
</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 =
+
= Overview =
 +
 
 +
The proposed method for FSL works by first computing a tree of segments from a graph of pixel dissimilarities. A set of dense feature vectors is then computed, encoding regions of multiple sizes centered on each pixel. Feature vectors are aggregated and fed to a classifier which estimates the distribution of object categories in a segment. A subset of tree nodes that cover the image are selected to maximize the average "purity" of the class distributions (i.e. maximizing the likelihood that each segment will contain a single object). The convolutional network feature extractor is trained end-to-end from raw pixels, so there is no need for engineered features.
 +
 
 +
There are five main ingredients to this new method for FSL:
 +
 
 +
1. Trainable, dense, multi-scale feature extraction
 +
2. Segmentation tree
 +
3. Regionwise feature aggregation
 +
4. Class histogram estimation
 +
5. Optimal purity cover
 +
 
 +
The three main contributions of this paper are:
 +
 
 +
1. Using a multi-scale convolutional net to learn good features for region classification
 +
2. Using a class purity criterion to decide if a segment contains a single object, as opposed to several objects, or part of an object
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3. An efficient procedure to obtain a cover that optimizes the overall class purity of a segmentation
  
 
= Model =
 
= Model =

Revision as of 16:15, 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).

Overview

The proposed method for FSL works by first computing a tree of segments from a graph of pixel dissimilarities. A set of dense feature vectors is then computed, encoding regions of multiple sizes centered on each pixel. Feature vectors are aggregated and fed to a classifier which estimates the distribution of object categories in a segment. A subset of tree nodes that cover the image are selected to maximize the average "purity" of the class distributions (i.e. maximizing the likelihood that each segment will contain a single object). The convolutional network feature extractor is trained end-to-end from raw pixels, so there is no need for engineered features.

There are five main ingredients to this new method for FSL:

1. Trainable, dense, multi-scale feature extraction 2. Segmentation tree 3. Regionwise feature aggregation 4. Class histogram estimation 5. Optimal purity cover

The three main contributions of this paper are:

1. Using a multi-scale convolutional net to learn good features for region classification 2. Using a class purity criterion to decide if a segment contains a single object, as opposed to several objects, or part of an object 3. An efficient procedure to obtain a cover that optimizes the overall class purity of a segmentation

Model

Results

SceneResultTableStanford.png

SceneResultTableSIFT.png

SceneResultTableBarcelona.png

SceneResultPictures.png

References

<references />