Authors:
Soumaya Nheri
1
;
Riadh Ksantini
2
;
Mouhamed Bécha Kaâniche
1
and
Adel Bouhoula
1
Affiliations:
1
Higher School of Communication of Tunis and University of Carthage, Tunisia
;
2
Higher School of Communication of Tunis, University of Carthage and University of Windsor, Tunisia
Keyword(s):
Handwritten Digits Recognition, Support Vector Machine, Kernel Covariance Matrix, One-Class Classification, Outlier Detection, Subclass Low Variances.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Document Imaging in Business
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Image-Based Modeling
;
Pattern Recognition
;
Software Engineering
Abstract:
Handwritten Digits Recognition (HWDR) is one of the very popular application in computer vision and it
has always been a challenging task in pattern recognition. But it is very hard practical problem and many
problems are still unresolved. To develop a high performance automatic HWDR, several learning algorithms
have been proposed, studied and modified. Much of the effort involved in Handwritten digits classification
with Support Vector Machine (SVM). More specifically, in the current study we are focusing on one-class
SVM (OSVM) approaches which are of huge interest for our problem. Covariance Guided OSVM (COSVM)
algorithm improves up on the OSVM method, by emphasizing the low variance directions. However, COSVM
does not handle multi-modal target class data. Thus, we design a new subclass algorithm based on COSVM,
which takes advantage of the target class clusters variance information. To investigate the effectiveness of
the novel Subclass COSVM (SCOSVM), we compared our pr
oposed approach with other methods based on
other contemporary one-class classifiers, on well-known standard MNIST benchmark datasets and Optical
Recognition of Handwritten Digits datasets. The experimental results verify the significant superiority of our
method.
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