TY  - JOUR
AU  - Albert Gordo
AU  - Florent Perronnin
AU  - Ernest Valveny
PY  - 2013//
TI  - Large-scale document image retrieval and classification with runlength histograms and binary embeddings
T2  - PR
JO  - Pattern Recognition
SP  - 1898
EP  - 1905
VL  - 46
IS  - 7
PB  - Elsevier
KW  - visual document descriptor
KW  - compression
KW  - large-scale
KW  - retrieval
KW  - classification
N2  - 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.
SN  - 0031-3203
UR  - http://dx.doi.org/10.1016/j.patcog.2012.12.004
L1  - http://refbase.cvc.uab.es/files/GPV2013.pdf
N1  - DAG; 600.042; 600.045; 605.203
ID  - Albert Gordo2013
ER  -