Authors:
Muhammad Rushdi
and
Jeffrey Ho
Affiliation:
University of Florida, United States
Keyword(s):
Texture classification, Texton, Sparse representation, Image dictionary.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Early Vision and Image Representation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Statistical Approach
Abstract:
This paper addresses the problem of texture classification under unknown viewpoint and illumination variations. We propose an approach that combines sparse K-SVD and texton-based representations. Starting from an analytic or data-driven base dictionary, a sparse dictionary is iteratively estimated from the texture data using the doubly-sparse K-SVD algorithm. Then, for each texture image, K-SVD representations of pixel neighbourhoods are computed and used to assign the pixels to textons. Hence, the texture image is represented by the histogram of its texton map. Finally, a test image is classified by finding the closest texton histogram using the chi-squared distance. Initial experiments on the CUReT database show high classification rates that compare well with Varma-Zisserman MRF results.