Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human-Centered Unsupervised Segmentation Fusion   Segmentation is generally an ill-posed problem since it results in multiple
solutions and is, therefore, hard to define ground truth data to evaluate
algorithms. The problem can be naively surpassed by using only one annotator
per image, but such acquisition doesn't represent the cognitive perception of
an image by the majority of people. Nowadays, it is not difficult to obtain
multiple segmentations with crowdsourcing, so the only problem that stays is
how to get one ground truth segmentation per image. There already exist
numerous algorithmic solutions, but most methods are supervised or don't
consider confidence per human segmentation. In this paper, we introduce a new
segmentation fusion model that is based on K-Modes clustering. Results obtained
from publicly available datasets with human ground truth segmentations clearly
show that our model outperforms the state-of-the-art on human segmentations.

11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph