Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Document image classification, with a specific view on applications of
  patent images   The main focus of this paper is document image classification and retrieval,
where we analyze and compare different parameters for the RunLeght Histogram
(RL) and Fisher Vector (FV) based image representations. We do an exhaustive
experimental study using different document image datasets, including the MARG
benchmarks, two datasets built on customer data and the images from the Patent
Image Classification task of the Clef-IP 2011. The aim of the study is to give
guidelines on how to best choose the parameters such that the same features
perform well on different tasks. As an example of such need, we describe the
Image-based Patent Retrieval task's of Clef-IP 2011, where we used the same
image representation to predict the image type and retrieve relevant patents.

25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an