Gabor-based texture classification through efficient prototype selection VIA normalized cut

Authors:
Jaime Melendez;Domenec Puig;Miguel Angel Garcia
Affiliations:
Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University;Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University;Department of Informatics Engineering, Autonomous University of Madrid
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
Cited 0

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised texture classification by integration of multiple texture methods and evaluation windows

Image and Vision Computing
Comparison of texture features based on Gabor filters

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.