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.
@article{arxiv.1601.03295,
title = {Document image classification, with a specific view on applications of patent images},
author = {Gabriela Csurka},
journal= {arXiv preprint arXiv:1601.03295},
year = {2016}
}
Comments
Paper submitted in 2014 as book chapter of Current Challenges in Patent Information Retrieval, Second edition by M. Lupu et al (eds.). To appear in 2016