Author:
Marc Joliveau
Affiliation:
CIRRELT - Université de Montréal, Canada
Keyword(s):
Image reduction, Image learning, Classification, Dimensionality problem, Image databases.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Vision and Perception
Abstract:
In the past decades, many domains collected great amounts of data, particularly multimedia files, and stored them in large databases. Therefore, area such as similarity search for image learning have received much attention in the recent years. This paper presents an innovative way to strongly reduce dimension and keep relations between components of an image data set. Our method is validated on the Mnist learning database containing 70000 pictures of handwritten digits. Results demonstrate that the proposed approach is very efficient. It allows to accurately classify, learn, and identify digits using very short computation time in comparison with those obtained with original full-size images.