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
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 evaluate
d 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.
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