Filtering for Texture Classification: A Comparative Study
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
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Texture classification using invariant ranklet features
Pattern Recognition Letters
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters
An optimum feature extraction method for texture classification
Expert Systems with Applications: An International Journal
Invariant texture classification for biomedical cell specimens via non-linear polar map filtering
Computer Vision and Image Understanding
Human Understandable Features for Segmentation of Solid Texture
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Journal of Visual Communication and Image Representation
Wavelet domain association rules for efficient texture classification
Applied Soft Computing
Bayesian texture classification and retrieval based on multiscale feature vector
Pattern Recognition Letters
Computers and Electrical Engineering
Journal of Visual Communication and Image Representation
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In this work, a new rotational and scale invariant feature set for textural image classification, combined invariant feature (CIF) set has been introduced. It is an integration of the crude wavelets like Gaussian, Mexican Hat and orthogonal wavelets like Daubechies to achieve a high quality rotational and scale invariant feature set. Also it is added with features obtained using the newly proposed weighted smoothening Gaussian filter masks to improve the classification results. To reduce the effect of overlapping features, the variations among the feature set are analyzed and the eigenfeatures are extracted to produce good classification result. The rotational invariance is achieved by using these two wavelets with their directional properties and the scale invariance is achieved by a method, which is an extension to fractal dimension (FD) features. The first- and second-order statistical parameter and entropy characterize the quality of the features extracted. Furthermore, a comparison that shows the higher recognition rate achieved with the newly proposed method for the set of 6720 samples collected from 105 different textures of Brodatz, Vistek, Indezine databases and some additional images collected from other resources of indexed and true color images is shown.