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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.
Document image database retrieval and browsing using texture analysis
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Daniel Keysers, Faisal Shafait, and Thomas M. Breuel · 2007
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Fisher kernels on visual vocabularies for image categorization
F. Perronnin and C. Dance · 2007
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Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle, and K.Q. Weinberger · 2009
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A bag-of-pages approach to unordered multi-page document classification
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A Gordo, F Perronnin, and E Valveny · 2012
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Icfhr 2012 competition on handwritten document image binarization
I. Pratikakis, B. Gatos, and K. Ntirogiannis · 2012
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Sparse kernel approximations for efficient classification and detection
A. Vedaldi and A. Zisserman · 2012
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Document Image Representation, Classification and Retrieval in Large-Scale Domains
Albert Gordo · 2013
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Large-scale document image retrieval and classification with runlength histograms and binary embeddings
Albert Gordo, Florent Perronnin, and Ernest Valveny · 2013
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Distance-Based Image Classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Good practice in large-scale learning for image classification
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Convolutional neural networks for document image classification
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Multimodal page classification in administrative document image streams
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Evaluation of deep convolutional nets for document image classification and retrieval
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