Learning Hierarchical Features for Scene Labeling. Farabet, C., Couprie, C., Najman, L., & LeCun, Y. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1915–1929, 2013.
Learning Hierarchical Features for Scene Labeling [link]Paper  doi  bibtex   
author = {Farabet, Cl{\'e}ment and Couprie, Camille and Najman, Laurent and LeCun, Yann},
title = {{Learning Hierarchical Features for Scene Labeling}},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2013},
volume = {35},
number = {8},
pages = {1915--1929},
annote = {Good point: hierarchy, multi resolution
Bad point: complicated training procedure.

This tree cover method is actually faster than CRF, as it simply involves finding best $C_k$ for each pixel, without caring about relationships between pixels. The set of C_k found is non-disjoint. See Fig 5's caption to understand it better.

The segmentation method used are not consistent. Sometimes they use gPb, a tree method, sometimes they produce multiple levels, using felzenszwalb and huttenlocher. But anyway.

pp. 1

> A striking characteristic of the system proposed here is that the use of a large contextual window to label pixels reduces the requirement for sophisticated postprocessing methods that ensure the consistency of the labeling.

However, I think postprocessing is sophisticated here as well

Section 4.1 Superpixel method

They trained a classifier in superpixel method. Maybe this is better than using classifer from the per-pixel classifer.

Seciton 4.2 CRF method

Eq. (15) since i and j are neighbors, using graideint of i or j in energy function shouldn't matter.

Section 4.3.2 Cover method.

To compute S, You always first restrict feature map vectors at locations covered by $C_k$, and then do spatial pyramid pooling with 3x3 (See Fig 6 caption) bins. Then we can predict its class to get purity.},
keywords = {deep learning},
doi = {10.1109/TPAMI.2012.231},
read = {Yes},
rating = {3},
date-added = {2017-02-17T21:27:20GMT},
date-modified = {2017-02-21T15:07:35GMT},
url = {http://ieeexplore.ieee.org/document/6338939/},
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uri = {\url{papers3://publication/doi/10.1109/TPAMI.2012.231}}

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