Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Content-aware media retargeting based on deep importance map   We present a neural network to estimate the visual information of important
pixels in image and video, which is used in content-aware media retargeting
applications. Existing techniques are successful in proposing retargeting
methods. Yet, the serious distortion and the shrunk problem in the retargeted
results still need to be investigated due to the limitations in the methods
used to analyze visual attention. To accomplish this, we propose a network to
define the importance map, which is sufficient to describe the energy of the
significant regions in image/video. With this strategy, more ideal results are
obtained from our system. Besides, the objective evaluation presented in this
paper shows that our media retargeting system can achieve better and more
plausible results than those of other works. In addition, our proposed
importance map performs well in the enlarge operator and on the
"difficult-to-resize" images.

36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th