A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Reduced Multidimensional Co-Occurrence Histograms in Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Texture Features and Their Use in Automatic Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture for Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification via conditional histograms
Pattern Recognition Letters
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
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters
Computers and Industrial Engineering
Bayesian texture classification and retrieval based on multiscale feature vector
Pattern Recognition Letters
A robust texture descriptor using multifractal analysis with Gabor filter
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Computers and Electrical Engineering
PCA plus LDA on wavelet co-occurrence histogram features: application to CBIR
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Noise tolerant local binary pattern operator for efficient texture analysis
Pattern Recognition Letters
An automatic computer-aided diagnosis system for liver tumours on computed tomography images
Computers and Electrical Engineering
HMM-based script identification for OCR
Proceedings of the 4th International Workshop on Multilingual OCR
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In this paper, we propose a novel texture feature extraction method based on the co-occurrence histograms of wavelet decomposed images, which capture the information about relationships between each high frequency subband and that in low frequency subband of the transformed image at the corresponding level. The correlation between the subbands at the same resolution exhibits a strong relationship, indicating that this information is significant for characterizing a texture. The classification performance is tested on a set of 32 Brodatz textures using the different wavelet filter banks for the proposed feature set. The results are compared with those obtained by using the Gabor filters and the method proposed by Montiel et al. [Montiel, E., Aguado, A.S., Nixon, M.S., 2005. Texture classification via conditional histograms. Pattern Recognition Lett. 26, 1740-1751]. The proposed and the Gabor features are then used in the identification of the script of a machine printed document. The scheme has been tested on eight Indian language scripts including English. It is found to be robust to the skew generated in the process of scanning a document. The experiments are also performed on the images with orientations of different angles and with varying coverage of text. The classification performance is analyzed using the k-NN classifier. The experimental results demonstrate the effectiveness of the proposed texture features in achieving the improved classification performance.