Rotation and Scale Invariant Texture Classification

Texture classification is very important in image analysis. Content based image retrieval, inspection of surfaces, object recognition by texture, document segmentation are few examples where texture classification plays a major role. Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O (n*log n) complexity. The experimental results, based on different testing data sets for images from Brodatz album with different orientations and scales, show that the implemented classification scheme outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.09 percent

Rotation and scale invariant texture classification using log polar wavelet energy signatures

Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This thesis proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures.The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So,the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n*log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a modified Mahalanobis classifier to classify a set of 12 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the implemented classification scheme using log- polar wavelet signatures outperforms other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 87.59 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features

Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets

Author name used in this publication: Kin-Man Lam2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Scale Selective Extended Local Binary Pattern for Texture Classification

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

Influence of color spaces over texture characterization

Images are generally represented in the RGB color space. This is the model commonly used for most cameras and for displaying on computer screens. Nevertheless, the representation of color images using this color space has some important drawbacks for image analysis. For example, it is a non-uniform space, that is, measured color differences are not proportional to the human perception of such differences. On the other hand, HSI color space is closer to the human color perception and CIE Lab color space has been defined to be approximately uniform. In this work, the influence of the color space for color texture characterization is studied by comparing Lab, HSI, and RGB color spaces. Their effectiveness is analyzed regarding their influence over two different texture characterization methods: DFT features and co-occurrence matrices. The results have shown that involving color information into texture analysis improves the characterization significantly. Moreover, Lab and HSI color spaces outperform RG

Classication of Breast Cancer Histopathological Images using Adaptive Penalized Logistic Regression with Wilcoxon Rank Sum Test

Classication of the histopathological image is an important problem indiagnosis and treatment. The problem of selecting the most useful fea-tures from thousands of candidates is a key problem in classication of thehistopathological image. In this paper, an adaptive penalized logistic regres-sion is proposed, with the aim of identication features, by combining thelogistic regression with the weighted L1-norm. Our proposed method is ex-perimentally tested and compared with state-of-the-art methods based on apublicly recent breast cancer histopathological image datasets. The resultsshow that the proposed method signicantly outperforms three competitormethods in terms of overall classication accuracy and the number of selectedfeatures