@Article{AlbertGordo2013,
author="Albert Gordo
and Florent Perronnin
and Ernest Valveny",
title="Large-scale document image retrieval and classification with runlength histograms and binary embeddings",
journal="Pattern Recognition",
year="2013",
publisher="Elsevier",
volume="46",
number="7",
pages="1898--1905",
optkeywords="visual document descriptor",
optkeywords="compression",
optkeywords="large-scale",
optkeywords="retrieval",
optkeywords="classification",
abstract="We present a new document image descriptor based on multi-scale runlengthhistograms. This descriptor does not rely on layout analysis and can becomputed efficiently. We show how this descriptor can achieve state-of-theartresults on two very different public datasets in classification and retrievaltasks. Moreover, we show how we can compress and binarize these descriptorsto make them suitable for large-scale applications. We can achieve state-ofthe-art results in classification using binary descriptors of as few as 16 to 64bits.",
optnote="DAG; 600.042; 600.045; 605.203",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2306), last updated on Thu, 21 Jan 2016 10:37:56 +0100",
issn="0031-3203",
doi="10.1016/j.patcog.2012.12.004",
opturl="http://dx.doi.org/10.1016/j.patcog.2012.12.004",
file=":http://refbase.cvc.uab.es/files/GPV2013.pdf:PDF"
}