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Authors: Radu Tudor Ionescu 1 ; Andreea-Lavinia Popescu 2 and Dan Popescu 2

Affiliations: 1 University of Bucharest, Romania ; 2 Politehnica University of Bucharest, Romania

ISBN: 978-989-758-089-5

Keyword(s): Autocorrelation, Image Autocorrelation, Optical Character Recognition, Digit Recognition, Patch-based Method, Image Classification

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Shape Representation and Matching

Abstract: The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image for a broad range of applications from texture analysis to grain density estimation. In this paper, a novel approach of capturing the autocorrelation of an image is proposed. More precisely, the autocorrelation is recorded in a set of features obtained by comparing pairs of patches from an image. Each feature stores the euclidean distance between a particular pair of patches. Although patches contain contextual information and have advantages in terms of generalization, most of the patch-based techinques used in image processing are heavy to compute with current machines. Therefore, patches are selected using a dense grid over the image to reduce the number of features. This approach is termed Patch Autocorrelation Features (PAF). The proposed approach is evaluated in a series of handwritten digit recognition experiments using the popular MNIST data set. The Patch Autocorrelation Features are compared with the euclidean distance using two classification systems, namely the k-Nearest Neighbors and Support Vector Machines. The empirical results show that the feature map proposed in this work is always better than a feature representation based on raw pixel values, in terms of accuracy. Furthermore, the results obtained with PAF are comparable to other state of the art methods. (More)

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Paper citation in several formats:
Ionescu, R.; Popescu, A. and Popescu, D. (2015). Patch Autocorrelation Features for Optical Character Recognition.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 419-426. DOI: 10.5220/0005289804190426

@conference{visapp15,
author={Radu Tudor Ionescu. and Andreea{-}Lavinia Popescu. and Dan Popescu.},
title={Patch Autocorrelation Features for Optical Character Recognition},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={419-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005289804190426},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Patch Autocorrelation Features for Optical Character Recognition
SN - 978-989-758-089-5
AU - Ionescu, R.
AU - Popescu, A.
AU - Popescu, D.
PY - 2015
SP - 419
EP - 426
DO - 10.5220/0005289804190426

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