Face recognition based on curvelets and local binary pattern features via using local property preservation

With huge and ever-growing products in the factory, image retrieval can help the worker retrieve the same, or similar, existing products rapidly and accurately to guide production. In this paper, an effective method based on Fourier transform and local binary pattern is proposed to improve the retrieval efficiency of wool fabric. After capturing the fabric image, histogram equalization was implemented on the value of the Hue, Saturation, Value (HSV) mode to enhance the contrast. Subsequently, Fourier transform together with local binary pattern operator were performed to obtain the frequency spectrum and the local binary pattern, respectively. Each frequency spectrum was divided into 22 rings with the same width, and the standard deviation of the frequencies in each ring was calculated as a Fourier feature. Distinct output values of each local binary pattern were counted and normalized as local binary pattern features. Finally, Euclidean distance was adopted to measure the similarity based on the Fourier feature and local binary pattern feature. Twenty thousand wool fabric images were captured to demonstrate the efficacy of the proposed method. Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.

Hyperdimensional (HD) computing is a form of brain-inspired computing which can be applied to numerous classification problems. In past research, it has been shown that seizures can be detected from electroencephalograms (EEG) with high accuracy using local binary pattern (LBP) encoding. This paper explores applicability of binary HD computing to seizure detection from intra-cranial EEG (iEEG) data from the Kaggle seizure detection contest based on using both LBP and power spectral density (PSD) features. In the PSD method, three novel approaches to HD classification are presented for both selected features and all features. These are referred as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single classifier long hypervector</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple classifiers</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single classifier short hypervector</i> . To visualize the quality of classification of test data, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hypervector distance</i> plot is introduced that plots the Hamming distance of the query hpervectors from one class hypervector <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vs.</i> that from the other. Simulation results show that: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1)</i> . LBP method offers an average 80.9% test accuracy, 71.9% sensitivity, 81.4% specificity and 76.6% test AUC whereas the PSD method can achieve an average of 91.0% test accuracy, 81.8% sensitivity, 92.0% specificity and 86.9% test AUC. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2)</i> . The average seizure detection latency is 2.5s for LBP method and is 4.5s for the PSD methods. This average latency, less than 5s, is a relevant parameter for fast drug delivery, indicating that both LBP and PSD methods are able to detect the seizures in a timely manner. The performance using selected PSD features is better than that using all features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3)</i> . It is shown that the dimensionality of the hypervector can be reduced to 1, 000 bits for LBP and PSD methods from 10, 000. Futhermore, for some approaches of selected features, the dimensionality of the hypervector can be reduced to 100 bits.

Recently, the local binary patterns (LBP) have been widely used in the texture classification. The LBP methods obtain the binary pattern by comparing the gray scales of pixels on a small circular region with the gray scale of their central pixel. The conventional LBP methods only describe microstructures of texture images, such as edges, corners, spots and so on, although many of them show good performances on the texture classification. This situation still could not be changed, even though the multi-resolution analysis technique is adopted by LBP methods. Moreover, the circular sampling region limits the ability of the conventional LBP methods in describing anisotropic features. In this paper, we change the shape of sampling region and get an extended LBP operator. And a multi-structure local binary pattern (Ms-LBP) operator is achieved by executing the extended LBP operator on different layers of an image pyramid. Thus, the proposed method is simple yet efficient to describe four types of structures: isotropic microstructure, isotropic macrostructure, anisotropic microstructure and anisotropic macrostructure. We demonstrate the performance of our method on two public texture databases: the Outex and the CUReT. The experimental results show the advantages of the proposed method.

Texture is the core element in numerous computer vision applications. The objective of this paper is to present a novel methodology for learning and recognizing textures. A local binary pattern (LBP) operator offers an efficient way of analyzing textures. A multi-level local binary pattern operator which is an extension of LBP is proposed for extracting texture feature from the images. The operator finds the association of LBP operators at multiple levels. This association helps to identify macro features. Depending on the size of the operator, octets are framed and LBP responses of the octets are noted. Their occurrence histograms are combined to frame the texture descriptor. The proposed operator is gray-scale invariant. The operator is computationally simple since it can be realized with few operations in the local neighborhood. A non-parametric statistic named G-Statistic is used in the classification phase. The classifier is trained with images of known texture class to build a model for that class. Experimental results prove that the approach provides good discrimination between the textures.

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

Based on pattern uniformity measure and the number of ones in the Local Binary Pattern (LBP) codes, this paper proposes an Improved Local Binary Pattern (ILBP) operator to describe local image texture more effectively. The ILBP operator discovers an important group of basic primitives such as lines, T-junctions, and cross-intersections, which are ignored by uniform LBP operator. Such local primitives are as crucial as those represented by uniform patterns for recognition tasks. The resulting ILBP feature is more discriminative than traditional LBP feature although they are both invariant in terms of monotonic gray-scale variation and rotation transformation.

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%).

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

Most image classification algorithms consist of feature extraction and image classification. Hence, feature extraction is a critical step for obtaining an accurate classification result. In this study, we perform the classification experiments based on the image features extracted using Local Binary Pattern operator (LBP), Discrete Wavelet Transforms (DWT) and Color Features (CF) for image classification. A comparison is made using the different combination of features; 1) LBP features only, 2) LBP and DWT features, and 3) LBP, DWT and CF features with a slight modification. Our goal is to determine what types of features are useful for improving the classification results. The Linear Support Vector Machine (SVM) algorithm is used to classify each pixel into a class based on the features used to obtain a classified image. Preliminary experimental results show that the hybrid of DWT, LBP, and CF gives the highest accuracy.

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector.

The aim of this research was to determine an acceptable method of texture feature extraction that could be used for a visual inspection of ceramic tiles quality. Traditional extraction of statistical features has been carried out as well as feature extraction based on local binary pattern operators. For the purpose of testing the proposed methods good and defective ceramic tiles have been identified. The obtained results indicate a possibility for developing a system of a visual inspection of ceramic tiles quality and point out local binary pattern operators as a powerful tool applicable in this particular case.

The COVID-19 disease is one of the most severe health disasters encountered in the world recently. COVID-19 disease is a highly fatal disease that spreads quickly through the respiratory tract. The most accurate method for diagnosing COVID-19 disease is Reverse Transcription Polymerase Chain Reaction (RT-PCR). Although this method gives more precise results, it is extremely costly in terms of cost and processing time. To overcome this issue, many studies are being carried out, particularly on diagnosis via lung images. Deep learning, artificial intelligence, and machine learning methods are applied in image enhancement studies, and powerful results are obtained. Within the scope of this study, texture analysis methods, which are part of the image enhancement methods, are examined. Among these methods, the local binary pattern (LBP) method has a special place. Due to the extremely high success rate of the LBP method, many successful derivative methods have been derived from this method. The paper includes the developed quadstar local binary pattern (QS-LBP) method derived from the standard LBP method. Moreover, machine learning (ML) algorithms are used to process the features extracted from two different models using the QS-LBP method. Extremely high analysis values have been obtained from the hybrid method consisting of the developed models and ML methods, accuracy is 99.21%, kappa is 96.2%, ROC area is 99.8%, F-measure is 99.6 %, precision is 99.5%, and finally recall is 99.8%, respectively. Using the hybrid technique suggested in the paper, diagnosing COVID-19 disease in lung images has been carried out exceedingly successfully.

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Serving as an effective texture description operator, local binary pattern (LBP) has been applied in the visible face recognition successfully. In order to enhance the recognition performance, the technology of face recognition depending on the near infrared (NIR) image has attracted extensive attention in the recent years. Although the characteristics of LBP have been researched in optical image thoroughly, the development of these do not catch enough attention in the NIR image at all. Therefore, in our paper, we study the characteristics of LBP from the following two aspects in NIR image. On one hand, we come to the probability distribution of various patterns of LBP in the NIR facial image. On the other hand, we discuss the influence to LBP caused by the illumination change in facial image.