Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spatially Constrained Location Prior for Scene Parsing   Semantic context is an important and useful cue for scene parsing in
complicated natural images with a substantial amount of variations in objects
and the environment. This paper proposes Spatially Constrained Location Prior
(SCLP) for effective modelling of global and local semantic context in the
scene in terms of inter-class spatial relationships. Unlike existing studies
focusing on either relative or absolute location prior of objects, the SCLP
effectively incorporates both relative and absolute location priors by
calculating object co-occurrence frequencies in spatially constrained image
blocks. The SCLP is general and can be used in conjunction with various visual
feature-based prediction models, such as Artificial Neural Networks and Support
Vector Machine (SVM), to enforce spatial contextual constraints on class
labels. Using SVM classifiers and a linear regression model, we demonstrate
that the incorporation of SCLP achieves superior performance compared to the
state-of-the-art methods on the Stanford background and SIFT Flow datasets.

6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio