Rotation Invariant Texture Features Research Articles

Image segmentation from scale and rotation invariant texture features from the double dyadic dual-tree complex wavelet transform

A goal of image segmentation is to divide an image into regions that have some semantic meaning. Because regions of semantic meaning often include variations in colour and intensity, various segmentation algorithms that use multi-pixel textures have been developed. A challenge for these algorithms is to incorporate invariance to rotation and changes in scale. In this paper, we propose a new scale and rotation invariant, texture-based segmentation algorithm, that performs feature extraction using the Dual-Tree Complex Wavelet Transform (DT-CWT). The DT-CWT is used to analyse a signal at, and between, dyadic scales. The performance of image segmentation using this new method is compared with existing techniques over different imagery databases with operator produced ground truth data. Compared with previous algorithms, our segmentation results show that the new texture feature is capable of performing well over general images and particularly well over images containing objects with scaled and rotated textures.

Pulse-coupled neural networks and one-class support vector machines for geometry invariant texture retrieval

The pulse-coupled neural network (PCNN) has been widely used in image processing. The outputs of PCNN represent unique features of original stimulus and are invariant to translation, rotation, scaling and distortion, which is particularly suitable for feature extraction. In this paper, PCNN and intersecting cortical model (ICM), which is a simplified version of PCNN model, are applied to extract geometrical changes of rotation and scale invariant texture features, then an one-class support vector machine based classification method is employed to train and predict the features. The experimental results show that the pulse features outperform of the classic Gabor features in aspects of both feature extraction time and retrieval accuracy, and the proposed one-class support vector machine based retrieval system is more accurate and robust to geometrical changes than the traditional Euclidean distance based system.

Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme

The objective of this study is to investigate the use of pattern classification methods for distinguishing different types of brain tumors, such as primary gliomas from metastases, and also for grading of gliomas. The availability of an automated computer analysis tool that is more objective than human readers can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. A computer-assisted classification method combining conventional MRI and perfusion MRI is developed and used for differential diagnosis. The proposed scheme consists of several steps including region-of-interest definition, feature extraction, feature selection, and classification. The extracted features include tumor shape and intensity characteristics, as well as rotation invariant texture features. Feature subset selection is performed using support vector machines with recursive feature elimination. The method was applied on a population of 102 brain tumors histologically diagnosed as metastasis (24), meningiomas (4), gliomas World Health Organization grade II (22), gliomas World Health Organization grade III (18), and glioblastomas (34). The binary support vector machine classification accuracy, sensitivity, and specificity, assessed by leave-one-out cross-validation, were, respectively, 85%, 87%, and 79% for discrimination of metastases from gliomas and 88%, 85%, and 96% for discrimination of high-grade (grades III and IV) from low-grade (grade II) neoplasms. Multiclass classification was also performed via a one-vs-all voting scheme.

An efficient approach to texture‐based image retrieval

AbstractIn this article, we present an efficient approach for image retrieval based on the textural information of an image, such as orientation, directionality, and regularity. For this purpose, we apply the nonlinear modified discrete Radon transform to estimate these visual contents. We then utilize texture orientation to construct the rotated Gabor transform for extraction of the rotation‐invariant texture feature. The rotation‐invariant texture feature, directionality, and regularity are the main features used in the proposed approach for similarity assessment. Experimental results on a large number of texture and aerial images from standard databases show that the proposed schemes for feature extraction and image retrieval significantly outperform previous works, including methods based on the MPEG‐7 texture descriptors. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 295–302, 2007

Texture classification based on EMD and FFT

Empirical mode decomposition (EMD) is an adaptive and approximately orthogonal filtering process that reflects human’s visual mechanism of differentiating textures. In this paper, we present a modified 2D EMD algorithm using the FastRBF and an appropriate number of iterations in the shifting process (SP), then apply it to texture classification. Rotation-invariant texture feature vectors are extracted using auto-registration and circular regions of magnitude spectra of 2D fast Fourier transform (FFT). In the experiments, we employ a Bayesion classifier to classify a set of 15 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing datasets for images with different orientations, show the effectiveness of the proposed classification scheme.

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.

Gaussian MRF rotation-invariant features for image classification

Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.

Rotation and scale invariant texture features using discrete wavelet packet transform

Novel rotation and scale invariant features are proposed in this paper using discrete wavelet packet transform. The classification performance is tested on a set of 15 Brodatz textures rotated in 12 directions and for five scales across an octave. The classification performance for different wavelet filter banks for the proposed rotation and scale invariant features is tested. An application of these features for script identification is illustrated.

Rotation invariant texture classification using even symmetric Gabor filters

Rotation invariant texture features are derived from the even symmetric Gabor filtered images of texture. The feature used is modified average absolute deviation from mean. Sixty Brodatz textures rotated in 12 different directions are classified using these features. Equal number of samples are used for training and testing phase. The percentage correct classification is 81.02. Segmentation of texture images with rotated textures is demonstrated using the features.

Rotation-invariant texture feature for image retrieval

An effective rotation-invariant polar-wavelet texture feature for image retrieval was proposed. The feature extraction process involves a polar transform followed by an adaptive row shift invariant wavelet packet transform. The polar transform converts a given image into a rotation-invariant but row-shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation-invariant wavelet coefficients with respect to an information cost function. An energy signature is computed for each subband of these wavelet coefficients. In order to reduce feature dimensionality, only the most dominant polar-wavelet energy signatures are selected as feature vector for image retrieval. The whole feature extraction process is quite efficient and involves only O( n·log n) complexity. Experimental results show that this rotation-invariant texture feature is effective and outperforms the other image retrieval algorithms.

A Survey of Rotation Invariant Texture Classification Methods

In this survey, the advances in rotation invariant texture classification techniques are discussed. The overall evolution of this niche from nascent to reasonably mature field is studied. It is very difficult to include all the works in such a paper; an attempt is made to include representative works of major methods. Markov Random Field based schemes are computationally involved. Methods based on wavelet and multichannel filtering are used for rotation invariant texture classification. Visual information retrieval system requires a robust and computationally less involved rotation invariant texture features. This area would be pursued vigorously in near future.

EFFICIENT ROTATION INVARIANT TEXTURE FEATURES FOR CONTENT-BASED IMAGE RETRIEVAL

An efficient approach to the extraction of rotation invariant texture features is presented. Histograms of intensity gradient directions are compiled. Rotation invariant features are extracted by taking the Fourier expansion of the histogram. The method is applied to image database annotation and content based retrieval. With a database of over 400 randomly rotated images all textures are correctly identified on the return of two classifications. On presentation of a texture to the image retrieval system nine out of ten images returned from the search are of the same texture as the query image. Its simplicity and accuracy render the method highly suited to applications such as content-based image retrieval. The method requires no human intervention. Extensive experimental results are included to demonstrate the performance of the method in texture classification and content-based retrieval.

Rotation invariant texture features and their use in automatic script identification

Concerns the extraction of rotation invariant texture features and the use of such features in script identification from document images. Rotation invariant texture features are computed based on an extension of the popular multi-channel Gabor filtering technique, and their effectiveness is tested with 300 randomly rotated samples of 15 Brodatz textures. These features are then used in an attempt to solve a practical but hitherto mostly overlooked problem in document image processing-the identification of the script of a machine printed document. Automatic script and language recognition is an essential front-end process for the efficient and correct use of OCR and language translation products in a multilingual environment. Six languages (Chinese, English, Greek, Russian, Persian, and Malayalam) are chosen to demonstrate the potential of such a texture-based approach in script identification.