Document image classification, with a specific view on applications of patent images
It addresses the need for robust image representations in document classification, particularly for patent retrieval, but is incremental as it focuses on parameter tuning of existing methods.
This paper tackles document image classification and retrieval by analyzing parameters for RunLength Histogram and Fisher Vector representations across multiple datasets, including MARG benchmarks and Clef-IP 2011 patent images, aiming to provide guidelines for parameter selection to improve performance on diverse tasks.
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.