Comparative evaluation of classical methods, optimized gabor filters and LBP for texture feature selection and classification

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, Tarragona, Spain;Intelligent Robotics and Computer Vision Group, Department of Computer Science and Mathematics, Rovira i Virgili University, Tarragona, Spain;Department of Informatics Engineering, Autonomous University of Madrid, Madrid, Spain
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

Citing 7
Cited 1

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
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Effects of Different Gabor Filter Parameters on Image Retrieval by Texture

MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Automatic texture feature selection for image pixel classification

Pattern Recognition
Supervised texture classification by integration of multiple texture methods and evaluation windows

Image and Vision Computing
Color and texture descriptors

IEEE Transactions on Circuits and Systems for Video Technology

The leiden augmented reality system (LARS)

ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper builds upon a previous texture feature selection and classification methodology by extending it with two state-of-the-art families of texture feature extraction methods, namely Manjunath & Ma's Gabor wavelet filters and Local Binary Pattern operators (LBP), which are integrated with more classical families of texture filters, such as co-occurrence matrices, Laws filters and wavelet transforms. Results with Brodatz compositions and outdoor images are evaluated and discussed, being the basis for a comparative study about the discrimination capabilities of those different families of texture methods, which have been traditionally applied on their own.