Scale Invariant Texture Classification Research Articles

Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Fusing DTCWT and LBP Based Features for Rotation, Illumination and Scale Invariant Texture Classification

Classification of texture images with different orientation, illumination, and scale changes is a challenging problem in computer vision and pattern recognition. This paper proposes two descriptors and uses them jointly to fulfill such task. One can obtain an image pyramid by applying dual-tree complex wavelet transform (DTCWT) on the original image, and generate local binary patterns (LBP) in DTCWT domain, called LBPDTCWT, as local texture features. Moreover, log-polar (LP) transform is applied on the original image, and the energies of DTCWT coefficients on detail subbands of the LP image, called LPDTCWTE, are taken as global texture features. We fuse the two kinds of features for texture classification, and the experimental results on benchmark data sets show that our proposed method can achieve better performance than other the state-of-the-art methods.

Efficient levels of spatial pyramid representation for local binary patterns

Local binary patterns (LBPs) are a well‐known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi‐level and multi‐resolution (ML + MR) approach and the ML, MR and multi‐band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi‐2 similarity measurement with Czekannowski improves the accuracy of texture classification.

Rotation Scale Invariant Texture Classification for a Computational Engine

Texture analysis is a highly significant area in the arena of computer vision and connected pitches. Not the least, classification is also equally important and laudable zone in the area of understanding the texture pattern and is gaining a lot of interest among the researchers in the field of computer vision. It finds a widespread application in area of pattern classification, robotic applications, textile industries etc. In this study, rotation invariant texture has been analyzed and a novel Rotation scale invariant texture classification algorithm has been proposed and tested which is found to be very efficacious and improved results are obtained with the same. The proposed algorithm has been made to undergo testing with standard data sets as UMD dataset, Vision Texture (VisTex), UIUC dataset. The results are discussed clearly with a line of justification being drawn.

Rotation and scale invariant texture classification by compensating for distribution changes using covariate shift in uniform local binary patterns

A novel rotation and scale invariant texture classification methodology is proposed based on distribution matching in higher dimensional space. Feature extraction is performed by using uniform local binary patterns (uLBPs) in which the rotation and scale changes in an image cause shifts in the underlying uLBP histograms. To compensate for these shifts at the classification layer, the distributions of training and testing data using kernel methods are estimated and means of the two distributions in the transformed domain using importance weights are matched. These calculated importance weights are used in the standard support vector machines to compensate for the shift in the distributions. The proposed method is used for classifying the images in the Brodatz texture database demonstrating the effectiveness of the proposed methodology.

Scale invariant texture classification via sparse representation

Scale change exists very commonly in real-world textural images which remains one of the biggest challenges in texture classification due to the tremendous changes involved in texture appearance. While most research efforts have been devoted to extracting various scale invariant features, these methods are either unsuitable to describe a texture or unable to handle the situations where a large amount of scale change exists. Other works attempt to avoid scale invariant feature extraction by generating a set of multi-scale representations from training images for classification, but they are not only computation intensive but also limited to dealing with small scale changes between training images and test images. In this paper we investigate the scaling properties of textures and introduce a low dimensional linear subspace for the multi-scale representations of a texture, in which the collaboration between the multi-scale representations is beneficial for the scale invariant texture classification. We therefore propose a new scale invariant texture classification framework without extracting scale invariant features, by using a sparse representation technique to model the multi-scale representations of a texture and taking the advantages of collaboration between them for classification. Specifically, a multi-scale dictionary is constructed from the Gaussian-pyramid-generated scale space of a small set of training images at one scale, and then the test images at arbitrary scales are classified via a modified sparse representation based classification method. Experiments on two benchmark texture databases show that the proposed method is able to deal with large scale changes between the training images and the test images and achieve comparative results to the state-of-the-art approaches for the classification of textures with various variations, especially scale.

Texture Classification Using Rotation- and Scale-Invariant Gabor Texture Features

This letter introduces a novel approach to rotation and scale invariant texture classification. The proposed approach is based on Gabor filters that have the capability to collapse the filter responses according to the scale and orientation of the textures. These characteristics are exploited to first calculate the homogeneous texture of images followed by the rearrangement of features as a two-dimensional matrix (scale and orientation), where scaling and rotation of images correspond to shifting in this matrix. The shift invariance property of discrete fourier transform is used to propose rotation and scale invariant image features. The performance of the proposed feature set is evaluated on Brodatz texture album. Experimental results demonstrate the superiority of the proposed descriptor as compared to other methods considered in this letter.

Rotation Invariant Texture Classification using Fuzzy Logic

this paper, we develop a scale invariant texture classification method based on Fuzzy logic. It is applied for the classification of texture images. Texture is a common property of any surface having uncertainty. Two types of texture features are extracted one using Discrete Wavelet Transform (DWT) and other using Co-occurrence matrix. Co- occurrence features are obtained using DWT coefficients. Two features are obtained from each sub-band of DWT coefficients upto fifth level of decomposition and eight features are extracted from co-occurrence matrix of whole image and each sub-band of first level DWT decomposition. The fuzzy classification is achieved in two steps, fuzzification step, and rule generation step. The performance is measured in terms of Success Rate. This study showed that the proposed method offers excellent scale invariant texture classification Success Rate. Also wavelet features like standard deviation, combination of energy and standard deviation along with some proposed hybrid feature sets outperform the other feature sets. This success rate is comparatively high when compared with results published earlier.

Scale- and Rotation-Invariant Local Binary Pattern Using Scale-Adaptive Texton and Subuniform-Based Circular Shift

This paper proposes an effective scale- and rotation-invariant local binary pattern (LBP) feature for texture classification. A circular neighboring set of an image pixel is defined as a scale-adaptive texton by taking into account the fundamental local structure property of the pixel. The scale space of a texture image is derived by the Laplacian of the Gaussian and then employed to determine the optimal scale of each pixel reflecting the characteristic length of the corresponding structure and determining the radius of the scale-adaptive texton. Different pixels have different optimal scales, resulting in the scale invariance. Contrary to the traditional LBP features that usually ignore global spatial information, the proposed method also defines subuniform patterns of each uniform pattern to improve the discrimination. For each uniform pattern, the subuniform pattern with the maximum statistical value is defined as the dominant orientation subuniform pattern. It is moved to the first column, and the others are circularly shifted. Experimental results demonstrate a good discrimination capability of the proposed scale- and rotation-invariant LBP in texture classification. Particularly, the LBP based on the scale-adaptive texton is promising to be powerful for texture description and scale-invariant texture classification, and the circular shift subuniform LBP can further improve the performance in the rotation-invariant texture classification.

A Biologically Inspired Object Spectral-Texture Descriptor and Its Application to Vegetation Classification in Power-Line Corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this letter, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of images or objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSFs) of a pulse-coupled neural network, which is invariant to rotation, translation, and small scale changes. The proposed method is first evaluated in a rotation- and scale-invariant texture classification using the University of Southern California Signal and Image Processing Institute texture database. It is further evaluated in an application of vegetation species classification in power-line corridor monitoring using airborne multispectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective in representing the spectral-texture patterns of objects, and it shows better results than classic color histogram and texture features.

Wavelets-Based Feature Extraction for Texture Classification

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

Rotation and Scale-Invariant Texture Classification Using Log-Polar and Ridgelet Transform

Rotation and Scale-Invariant Texture Classification Using Log-Polar and Ridgelet Transform

Rotation- and scale-invariant texture classification using log-polar and ridgelet transforms

Classification of distorted texture images is a challenging and important problem in real world image analysis and understanding. This paper proposes a new texture characterization method which is ...

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

An efficient texture classification method is proposed that considers the effects of both the rotation and scale of texture im- ages. In our method, the Gabor wavelets are adopted to extract local features of an image and the statistical properties of its gray- level intensities are used to represent the global features. Then, an adaptive, circular orientation normalization scheme is proposed to make the feature invariant to rotation, and an elastic cross- frequency searching mechanism is devised to reduce the effect of scaling. Our method is evaluated based on the Brodatz album and the Outex database, and the experimental results show that it out- performs the traditional algorithms. © 2008 SPIE and

Real Coded Genetic Algorithm for Optimal Parameters Selection in Circular Mellin Feature Extractors

Texture is an important clue in region-based segmentation of images. Robust rotation and scale-invariant texture are important for digital image libraries and multimedia databases. Texture feature extraction operators, which comprise linear filtering followed by postprocessing, are considered. The CMF (Circular Mellin Feature) represents the spectral decomposition of the image scene in the polar-log coordinate system and is invariant to both scale and orientation of the target texture pattern. CMFs are used for rotation and scale-invariant texture classification. The image and CMFs are correlated, due to which the unique shift invariance property of the correlator architecture is also coupled. CMFs are based on log polar coordinate system. In this paper we present Real coded Genetic Algorithm (RcGA) for the parameter selection of CMFs. The simple genetic algorithm (GA) is search algorithms based on the mechanics of natural selection and genetics. It involves copying and swapping partial strings of data. A modified genetic algorithm is the RcGA, which basically employs real value vectors for representation of the chromosomes, and is widely applied to many optimization problems.

An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

Nowadays, texture classification becomes more important, as the computational power increases. The most important hardness of texture image analysis in the past was the deficiency of enough tools to characterize variety scales of texture images effectively. Recently, multi-resolution analysis such as Gabor filters, wavelet decompositions provide very good multi-resolution analytical tools for different scales of texture analysis and classification. In this paper, a Wavelet Neural Network based on Adaptive Norm Entropy (WNN-ANE) expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm (Best Wavelet Statistical Features (WSF)-Wavelet Co-occurrence Features (WCF)). Efficiently of proposed method was proved using exhaustive experiments conducted with Brodatz texture images.

Log-polar wavelet energy signatures for rotation and scale invariant texture classification

Classification of texture images is important in image analysis and classification. This paper 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 /spl middot/ 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 Mahalanobis classifier to classify a set of 25 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 proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.

Scale invariant texture classification by iterativemorphological decomposition

A new algorithm for scale invariant texture analysis is introduced. The method makes use of the iterative morphological decomposition to find a set of scale invariant parameters. Experiments show that very accurate result can be obtained for classifying eight natural textures.