Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-hypothesis contextual modeling for semantic segmentation   Semantic segmentation (i.e. image parsing) aims to annotate each image pixel
with its corresponding semantic class label. Spatially consistent labeling of
the image requires an accurate description and modeling of the local contextual
information. Segmentation result is typically improved by Markov Random Field
(MRF) optimization on the initial labels. However this improvement is limited
by the accuracy of initial result and how the contextual neighborhood is
defined. In this paper, we develop generalized and flexible contextual models
for segmentation neighborhoods in order to improve parsing accuracy. Instead of
using a fixed segmentation and neighborhood definition, we explore various
contextual models for fusion of complementary information available in
alternative segmentations of the same image. In other words, we propose a novel
MRF framework that describes and optimizes the contextual dependencies between
multiple segmentations. Simulation results on two common datasets demonstrate
significant improvement in parsing accuracy over the baseline approaches.

28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc