Cattle face recognition using local binary pattern descriptor

In today's world with the increase in pollution and other lifestyle changes of people, many individuals are prone to various cancer types. Out of these many cancer types Skin Cancer is the most common. Skin cancer is of two types i.e., benign and malignant. Timely detection of the skin cancer may lead to proper treatment and thus reduce the cost of treatment and hence reduces the damage to the skin-saving patients' life. For detecting skin cancer with high accuracy and very fast compared to the traditional methods this paper proposed the method of using texture-based features descriptors such as Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG). This research proposed the method of detecting skin cancer type from the RGB image of the target area of skin cancer. The proposed method used texture features of the image to detect skin cancer with help of LBP and HOG texture feature descriptors. Further Logistic Regression (LR) is applied for classifying whether skin cancer is benign or malignant. The results are very efficient as the accuracy achieved by utilizing LBP and HOG of RGB image data is 99.394%. A total of 3600 RGB Images have been used for such a study. The experimental analysis results obtained are compared with other techniques like Random Forest Classifier, Ada Boost Classifiers, and Logistic Regression and it is found that the proposed approach outperforms available algorithms.

High-resolution remote sensing images have rich texture information,and combined texture information and image spectral information can improve the recognition accuracy of surface feature.In this paper,a new extended Local Binary Patterns(LBP) texture was applied to the high-resolution images classification in comparison with classifications using spectral data only and using combined spectral data and LBP texture features.The results show that the extended LBP has a good anti-noise performance,and the classification of image including the extended LBP texture can achieve a higher accuracy than the classifications using spectral data alone and using combined spectral data and LBP texture features.

In this paper, we study how multidimensional local binary pattern (LBP) texture feature data can be visually explored and analyzed.The goal is to determine how true paper properties can be characterized with local texture features from visible light images.We utilize isometric feature mapping (Isomap) for the LBP texture feature data and perform non-linear dimensionality reduction for the data.These 2D projections are then visualized with original images to study data properties.Visualization is utilized in the manner of selecting texture models for unlabeled data and analyzing feature performance when building a training set for a classifier.The approach is experimented with simulated image data illustrating different paper properties and on-line transilluminated paper images taken from running paper web in the paper mill.The simulated image set is used to get quantitative figures of the performance while the analysis of real-world data is an example of semisupervised learning.

A non-supervised clustering based method for classifying paper according to its quality is presented. The method is simple to train, requiring minimal human involvement. The approach is based on Self-Organizing Maps and texture features that discriminate the texture of effectively. Multidimensional texture feature vectors are first extracted from paper images. The dimensionality of the data is then reduced by a Self-Organizing Map (SOM). In dimensionality reduction, the feature data are projected to a two-dimensional space and clustered according to their similarity. The clusters represent different paper qualities and can be labeled according to the quality information of the training samples. After that, it is easy to find the quality class of the inspected paper by checking where a sample is placed in the low-dimensional space. Tests based on images taken in a laboratory environment from four different paper quality classes provided very promising results. Local Binary Pattern (LBP) texture features combined with a SOM-based approach classified the test data almost perfectly: the error percentage was only 0.2% with the multiresolution version of LBP and 1.6% with the regular LBP. The improvement to the previously used texture features in paper inspection is huge: the classification error is reduced over 40 times. In addition to the excellent classification accuracy, the method also offers a self-intuitive user interface and a synthetic view to the inspected data.

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.KeywordsLBPLocal binary patternLRBPLocal roughness binary patternSVMSupport vector machineKNN K-nearest neighbors

This paper proposes a texture feature which is applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Inspired by local binary pattern (LBP) texture features, a new variant of LBP feature, block based LBP (BLBP) is proposed. Instead of representing intensity differences between neighbors and a center pixel, BLBP feature extracts the intensity differences among certain blocks of the neighborhood around a pixel. Two different ways are proposed to organize the blocks: the spokes and the rings. By integrating spoke BLBP with ring BLBP features, very high classification accuracy is achieved using a neural network classifier. In one of our experiments which classifies 4310 OCM images into five tissue types, the classification accuracy increased from 81.7% to 92.4% when new features are used instead of the traditional LBP feature. In another experiment which classifies 46 large field OCM images as either benign or containing tumor, a classification accuracy of 91.3% is reached by using multi-scale BLBP features.

PLBP: An effective local binary patterns texture descriptor with pyramid representation

Osteoarthritis (OA) causes progressive degeneration of articular cartilage and pathological changes in subchondral bone. These changes can be assessed volumetrically using micro-computed tomography (\(\mu \)CT) imaging. The local descriptor, i.e. local binary pattern (LBP), is a new alternative solution to perform analysis of local bone structures from \(\mu \)CT scans. In this study, different trabecular bone samples were prepared from patients diagnosed with OA and treated with total knee arthroplasty. The LBP descriptor was applied to correlate the distribution of local patterns with the severity of the disease. The results obtained suggest the appearance and disappearance of specific oriented patterns with OA, as an adaptation of the bone to the decrease of cartilage thickness. The experimental results suggest that the LBP descriptor can be used to assess the changes in the trabecular bone due to OA.

The most expressive manner which human can reveal his emotional states is facial expression. Automatic facial expression recognition is an emerging field of study having extensive applications among which the human-computer interaction (HCI) has received lots of attentions in recent years. The features extracted from facial images, in order to recognize facial expressions, play an essential role in effectiveness of the facial image descriptors. Local binary pattern (LBP) texture descriptors have been known as simple, yet efficient descriptors which are noticeably used for extracting facial patterns from images. Recently, a generalized form of local binary pattern has been introduced which can offer a more precise image description than simple LBP descriptors. Consequently, it would be expected that taking the advantage of using these new LBP texture descriptors will produce more promising results in comparison with use of simple local binary pattern descriptors. In this paper, a novel method has been proposed for image feature extraction using these new image texture descriptors (generalized LBP); then, the obtained results have been compared to the results produced when applying simple LBP descriptors. Furthermore, K-NN and SVM have been used as classifiers in the proposed approach. Finally, a comparison between the proposed method and the existing local binary pattern algorithms for facial expression recognition concludes the superiority of the proposed algorithm over its existing counterparts.

Abstract. Developing a remote sensing-based monitoring system for detecting marine plastics requires a thorough investigation of their discrimination possibilities from other floating objects. To this end, this study aims to explore the ability to discriminate marine debris from other floating materials and sea features using high-resolution multispectral satellite data. To perform our analysis, we utilized the open-access Marine Debris Archive (MARIDA), which contains several marine classes on Sentinel-2 (S2) data and benchmarks machine learning frameworks. In particular, we investigated well-established spectral indices, GrayLevel Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) texture and other spatial features at multiple scales. A Random Forest (RF) classifier was also applied for the classification procedure, and the spectral and spatial features which contributed the most were underlined. The quantitative and qualitative assessment indicated that the spectral information alone is insufficient to distinguish marine plastic from other floating materials which exhibit similar spectral behavior, such as vessels. However, a strong potential even for challenging discrimination tasks is presented when combined with spatial information. By further evaluating our results qualitatively, significant insights are gained, and specific combinations are proposed for challenging floating materials discrimination.

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.

This paper proposes a novel approach in image compression based on Local Binary Pattern (LBP). LBP has already been used as a simple texture descriptor, labeling the image pixels by looking at the points surrounding a central point (usually on a 3×3 neighborhood) and examining whether these neighbors' color values are greater or less than the central point and accordingly assigning a binary value to the corresponding bit. The description of image's local pattern results in an eight-bit binary description, but in order to restore the image from such a LBP description, the value of each central pixel is also needed. These two pieces of information, i.e. the LBP description and the actual original value for each local neighborhood central pixel, are stored in a newly proposed Local Binary Compressed format, denoted .LBC, from which the image can be reconstructed by employing statistical methods, i.e. generating smaller or larger sets of random numbers to fill in the missing information within each local neighborhood, based on the LBP descriptor. Two statistical distributions were tested and, apart from the compression performance, a Structural Similarity Index Metric was used to evaluate the results.

The analysis of image feature robustness using cometcloud