Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Adaptive and Robust Filter Sets Using an Unsupervised
  Learning Framework   In this paper, we introduce an adaptive unsupervised learning framework,
which utilizes natural images to train filter sets. The applicability of these
filter sets is demonstrated by evaluating their performance in two contrasting
applications - image quality assessment and texture retrieval. While assessing
image quality, the filters need to capture perceptual differences based on
dissimilarities between a reference image and its distorted version. In texture
retrieval, the filters need to assess similarity between texture images to
retrieve closest matching textures. Based on experiments, we show that the
filter responses span a set in which a monotonicity-based metric can measure
both the perceptual dissimilarity of natural images and the similarity of
texture images. In addition, we corrupt the images in the test set and
demonstrate that the proposed method leads to robust and reliable retrieval
performance compared to existing methods.

12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio