Colour and rotation invariant textural features based on Markov random fields

Authors:
Pavel Vácha;Michal Haindl;Tomáš Suk
Affiliations:
Institute of Information Theory and Automation of the ASCR, 182 08 Prague, Czech Republic;Institute of Information Theory and Automation of the ASCR, 182 08 Prague, Czech Republic;Institute of Information Theory and Automation of the ASCR, 182 08 Prague, Czech Republic
Venue:
Pattern Recognition Letters
Year:
2011

Citing 28
Cited 3

A model-based method for rotation invariant texture classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification and segmentation using multiresolution simultaneous autoregressive models

Pattern Recognition
Reflectance and texture of real-world surfaces

ACM Transactions on Graphics (TOG)
Similarity Measures

IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years

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
Color constancy from physical principles

Pattern Recognition Letters - Special issue: Colour image processing and analysis
The illumination-invariant recognition of color texture

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Coefficient color constancy

Coefficient color constancy
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Gaussian MRF Rotation-Invariant Features for Image Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Approach to Texture Classification from Single Images

International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific Material Categorisation

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Image retrieval measures based on illumination invariant textural MRF features

Proceedings of the 6th ACM international conference on Image and video retrieval
Performance evaluation of local colour invariants

Computer Vision and Image Understanding
Material-specific adaptation of color invariant features

Pattern Recognition Letters
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

International Journal of Computer Vision
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features

SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Bidirectional Texture Function Modeling: A State of the Art Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dominant local binary patterns for texture classification

IEEE Transactions on Image Processing
Moments and Moment Invariants in Pattern Recognition

Moments and Moment Invariants in Pattern Recognition
Illumination invariant unsupervised segmenter

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Circular-Mellin features for texture segmentation

IEEE Transactions on Image Processing
Rotation-invariant texture classification using a complete space-frequency model

IEEE Transactions on Image Processing
Combining color and shape information for illumination-viewpoint invariant object recognition

IEEE Transactions on Image Processing
Rotation Moment Invariants for Recognition of Symmetric Objects

IEEE Transactions on Image Processing

Extending morphological covariance

Pattern Recognition
Comparative study of moment based parameterization for morphological texture description

Journal of Visual Communication and Image Representation
Texture databases - A comprehensive survey

Pattern Recognition Letters

Quantified Score

Hi-index	0.10

Visualization

Abstract

A visual appearance of natural materials significantly depends on acquisition circumstances, particularly illumination conditions and viewpoint position, whose variations cause difficulties in the analysis of real scenes. We address this issue with novel texture features, based on fast estimates of Markovian statistics, that are simultaneously rotation and illumination invariant. The proposed features are invariant to in-plane material rotation and illumination spectrum (colour invariance), they are robust to local intensity changes (cast shadows) and illumination direction. No knowledge of illumination conditions is required and recognition is possible from a single training image per material. The material recognition is tested on the currently most realistic visual representation - Bidirectional Texture Function (BTF), using CUReT and ALOT texture datasets with more than 250 natural materials. Our proposed features significantly outperform leading alternatives including Local Binary Patterns (LBP, LBP-HF) and texton MR8 methods.