Evaluation of the effects of Gabor filter parameters on texture classification
Pattern Recognition
Wavelet-based modeling of singular values for image texture classification
Machine Graphics & Vision International Journal
Image texture classification using wavelet packet transform and probabilistic neural network
Intelligent Data Analysis
Recycled paper visual indexing for quality control
Expert Systems with Applications: An International Journal
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Detection of object motion regions in aerial image pairs with a multilayer Markovian model
IEEE Transactions on Image Processing
Unsupervised texture segmentation using feature selection and fusion
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Neural Networks
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
Enhancing Gabor wavelets using volumetric fractal dimension
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Artificial immune multi-objective SAR image segmentation with fused complementary features
Information Sciences: an International Journal
Probabilistic rules for automatic texture segmentation
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Bark classification based on gabor filter features using RBPNN neural network
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
A generic approach to object matching and tracking
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
An active contour model guided by LBP distributions
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Decision fusion based unsupervised texture image segmentation
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Bark classification based on textural features using artificial neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Bark classification based on contourlet filter features using RBPNN
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Stereo imaging with uncalibrated camera
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Subband effect of the wavelet fuzzy C-means features in texture classification
Image and Vision Computing
Automatic skin lesion segmentation based on texture analysis and supervised learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
Gabor wavelets combined with volumetric fractal dimension applied to texture analysis
Pattern Recognition Letters
Hi-index | 0.02 |
A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. The fused feature set utilizes both the Gabor filter's capability of accurately capturing lower and mid-frequency texture information and the GLCP's capability in texture information relevant to higher frequency components. Evaluation methods include comparing feature space separability and comparing image segmentation classification rates. The fused feature sets are demonstrated to produce higher feature space separations, as well as higher segmentation accuracies relative to the individual feature sets. Fused feature sets also outperform individual feature sets for noisy images, across different noise magnitudes. The curse of dimensionality is demonstrated not to affect segmentation using the proposed the 48-dimensional fused feature set. Gabor magnitude responses produce higher segmentation accuracies than linearly normalized Gabor magnitude responses. Feature reduction using principal component analysis is acceptable for maintaining the segmentation performance, but feature reduction using the feature contrast method dramatically reduced the segmentation accuracy. Overall, the designed fused feature set is advocated as a means for improving texture segmentation performance.