Texture classification using rotation invariant models on integrated local binary pattern and Zernike moments

The local binary pattern (LBP) and its variants have shown the effectiveness in texture images classification, face recognition, and other applications. However, most of these LBP methods only focus on the histogram of LBP patterns and ignore the spatial contextual information between LBP patterns. In this paper, we propose a 2D-LBP method which uses a sliding window to count the weighted occurrence number of the rotation invariant uniform LBP pattern pairs to obtain the spatial contextual information. The multi-resolution 2D-LBP features can also be obtained when the radius of 2D-LBP is changed. At last, a two-stage classifier which acts as an ensemble learning step is followed to achieve an accurate classification by combining the predictions on each 2D-LBP with single resolution. Theoretical proof shows that the proposed 2D-LBP is a general framework and can be integrated on other LBP variants to derive new feature extraction methods. Experimental results show that, the proposed method achieves 99.71%, 97.09%, 98.48%, and 49.00% classification accuracy on the public “Brodatz,” “CUReT,” “UIUC,” and “FMD” texture image databases, respectively. Compared with the original LBP and its variants, the proposed method obtains higher classification accuracy under different cases, and simultaneously owns shorter time complexity.

SIFT (scale invariant feature transform) is one of the most robust and the widely used image matching algorithms based on local features. It has been used in the aspect of the cameras target matching. For its high complexity, this paper proposes a target matching method for multiple cameras by combining SIFT and the rotation-invariant LBP (local binary patterns). The object detection is obtained by improved Back-ground subtraction technique, and particle filter is used for region-based object tracking. The descriptor combining SIFT and the rotation-invariant LBP is calculated for every object region. The object matching is obtained by comparing the distance of feature matrix. Experimental results show that the proposed algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm, and it is robust in various scenes.

Local binary pattern (LBP) and its variants (e.g., LTP and CLBP) are powerful descriptors for texture analysis. However, most of these LBP-based methods are sensitive to inverse grayscale changes. To overcome this problem, we present a novel texture descriptor named Completed Grayscale-Inversion and Rotation Invariant Local Binary Pattern (CGRI-LBP). CGRI-LBP is based on the framework of CLBP which jointly encodes three components (i.e., the signs and magnitudes of local differences as well as central pixels) but with two significant improvements: 1) the sign information of local differences is encoded by a rotation-invariant complementary coding scheme, and 2) the intensity information of central pixels is encoded via a dominant intensity order measure. Extensive experiments on three texture databases (Outex, CUReT and KTH-TIPS) demonstrate that the proposed descriptor achieves the state-of-the-art classification performance in the presence of linear and even nonlinear grayscale-inversion changes.

This paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.

In this paper, rotation invariant local binary pattern and local contrast measure have been joined together for describing the texture feature. In addition to this, colour features have also been included in the feature vector of the object tracking algorithm. Thus the texture can be characterised by two orthogonal properties like, the spatial structure local binary pattern and the strength of the pattern contrast. Covariance matrix of the feature vector has been taken as the object descriptor in the proposed algorithm. The performance of the proposed method has been compared by different performance measures, such as the detection rate, tracking speed and coverage test. The proposed method has also been tested for various challenging situations, such as occlusion, camera motion, non-rigidity, and changes in illumination. The experimental results show that the proposed method outperforms the existing methods in terms of performance indices.

Application of local binary patterns in digital images to estimate botanical composition in mixed alfalfa–grass fields

To enhance the weakness of Local Binary Pattern (LBP) and its state-of-the-art variants, this letter presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index based texture descriptor is named as SI-LCvMSP. Contrarily to the original LBP and LCvMSP, SI-LCvMSP uses the shape index instead of the original texture image in the kernel function. The shape index is a differential calculation and it can be calculated from local second-order derivatives of texture images. It captures microstructure and macrostructure texture information mathematically. As textural features, we use multi-scale and multi-resolution shape index information as well as rotation-invariant uniform LBP. Thus, we obtain the discriminative feature representation schema to construct cross-scale joint coding. The proposed method has a high discriminability and is less sensitive to image transforms such as rotation and illumination. Experimental results show that the SI-LCvMSP descriptor can improve classification accuracy.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

In this paper, we present a novel image representation approach for classifying emphysema in computed tomography (CT) images of the lung. Our proposed method extends rotation invariant uniform local binary pattern (RIULBP) and local ternary pattern (LTP), which are extensively used in a variety of computer vision applications, into rotation invariant uniform local ternary pattern (RIULTP) with a human perception principle: Weber's law. In addition, by integrating the upper pattern and the lower pattern of the Weber-based RIULTP (WRIULTP), we further put forward the joint Weber-based rotation invariant uniform local ternary pattern (JWRIULTP), which allows for a much richer representation and also takes the comprehensive information of the image into account. The proposed methods are tested on the Outex database (texture database) and the Bruijne and S⊘rensen database (emphysema database). The results show the superiority of the proposed approaches to the state-of-the-art techniques for emphysema classification including rotation invariant local binary pattern (RILBP) and texton-based approach.

Scene classiflcation is an appealing and challenging problem in image processing and machine vision. Recently, Bag-of-visual-words (BOVW) method using pyramid matching scheme has shown remarkable performance for scene classiflcation. But this method deriving from local keypoints does not contain texture features which are rich in scene images. To further improves the classiflcation accuracy, this paper presents a new method combining Rotation Invariant Local Binary Patterns (RILBP) texture features and BOVW model in spatial pyramid matching framework. First, scene image is subdivided at difierent resolutions for constructing a spatial pyramid. Then based on scale invariant feature transform descriptor and K-means clustering, Pyramid Histogram of visual Words (PHOW) is extracted. And RILBP texture feature is extracted using the mean of a 3*3 neighborhood as threshold. Last we construct a composite kernel of spatial pyramid matching. We regard the keypoint features and texture features as two independent feature channels, and combine them to realize scene classiflcation using one-against-rest SVMs with the composite kernel. Experiments results on the three difierent scene datasets show that our method is efiective.

Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.

The Local Binary Patterns (LBP) feature extraction method is a theoretically and computationally simple and efficient methodology for texture analysis. The LBP operator is used in many applications such as facial expression recognition and face recognition. The original LBP is based on hard thresholding the neighborhood of each pixel, which makes texture representation sensitive to noise. In addition, LBP cannot distinguish between a strong and a weak pattern. In order to enhance the LBP approach, Fuzzy Local Binary Patterns (FLBP) is proposed. In FLBP, any neighborhood does not represented only by one code, but, it is represented by all existing codes with different degrees. In FLBP, any fuzzy Intersection and Union operators may be used. In this study, the following operators are applied and their results are compared together: Dot-Sum, Min-Max and normalized Min-Max. Based on the extensive experiments, the fuzzy Min-Max operators are more useful and can improve the accuracy in the application of Facial Expression Recognition (FER) about 4% (i.e., form 82.98% to 86.88%).

One of the most representative manners which human shows his inner feelings is through facial expressions. Automatic recognition of these gestures is of great importance in human computer interaction (HCI) applications. In order to automatically recognize facial expressions, local binary patterns (LBPs), which are powerful and computationally simple descriptors, have been extensively utilized for feature extraction from facial images. To improve image description accuracy, we have employed a new version of LBP, namely high order joint derivative local binary pattern (HJDLBP) in this paper which reveals spatial relations between patterns in different resolutions. Furthermore, a rotation invariant radial partitioning paradigm is utilized to localize LBP histogram calculation regions. Using radial partitioning instead of blocking eliminates head rotation effects in real world applications and improves classification accuracy. A bunch of support vector machines are trained by feature vectors to classify different facial expressions. To evaluate the proposed method we have used known and publicly available JAFFE dataset for experiments and compared the obtained results with results from state of the art approaches which use original LBP and rectangular partitioning. The comparison results show the effectiveness of the new descriptor in combination with radial partitioning for automatic facial expression categorization.

An effective rotation and scale invariant log-polar wavelet texture feature for image retrieval was proposed. The feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. The log-polar transform converts a given image into a rotation and scale 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 and scale 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 log-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/spl middot/log n) complexity. Experimental results show that this rotation and scale invariant texture feature is effective and outperforms the traditional wavelet packet signatures.

This study addresses the classification problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Generally, the method used for IIF analysis remains subjective, and depends too heavily on the experience and expertise of the physician. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. However, it still has large gap in recognition rates to the physical experts' one. This paper explores an approach in which the discriminative features of HEp-2 cell images in IIF are extracted and then, the patterns of the HEp-2 cell are identified using machine learning techniques. Motivated by the progress in the research field of computer vision, as a result of which small local pixel pattern distributions can now be highly discriminative, the proposed strategy employs a parametric probability process to model local image patches (textons: microstructures in the cell image) and extract the higher-order statistics of the model parameters for the image description. The proposed strategy can adaptively characterize the microtexton space of HEp-2 cell images as a generative probability model, and discover the parameters that yield a better fitting of the training space, which would lead to a more discriminant representation for the cell image. The simple linear support vector machine is used for cell pattern identification because of its low computational cost, in particular for large-scale datasets. Experiments using the open HEp-2 cell dataset used in the ICIP2013 contest validate that the proposed strategy can achieve a much better performance than the widely used local binary pattern (LBP) histogram and its extensions, rotation invariant co-occurrence LBP, and pairwise rotation invariant co-occurrence LBP, and that the achieved recognition error rate is even very significantly below the observed intralaboratory variability.