Weber Local Binary Pattern Research Articles

Skin cancer classification enabled mobile neuro fuzzy network and entropy with weber local binary pattern based for feature extraction

Skin cancer (SC) is the most quickly spreading diagnosis all over the world due to limited resources presented. Accurately diagnosing skin lesions in their early stages could help clinicians make better decisions by raising the likelihood of a cure before cancer develops. However, because to the imbalance and scarcity of most skin illness images used for training, it is challenging to achieve automatic skin cancer classification. Also, it experiences difficulties like high consumption of time. Deep learning (DL) modules are introduced to deal with these difficulties as well as it aids dermatologists in creating precise examinations. To conquer this concern, a novel entropy system for SC classification is developed by a hybrid network named as Mobile Neuro Fuzzy Network (MNFN), where MNFN is the fusion of MobileNet and Cascade Neuro fuzzy Network (cascade NFN). The input image is forwarded to the pre-processing unit, which is done by using a bilateral filter. Skin lesion segmentation is done by Squeeze U-SegNet. Feature extraction is performed using the proposed Weber Local Binary Pattern (WLBP) with entropy is also extracted. Then, data augmentation (DA) by oversampling scheme Synthetic Minority Oversampling Technique (SMOTE) is performed. Finally, SC classification is executed by employing a hybrid model MNFN. Here the SC is classified into various class labels. The analytic metrics used for MNFN namely, accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate(FPR) and False Negative Rate (FNR) gained suitable values of 0.930, 0.945, 0.931, 0.110 and 0.094.

Weber Global Statistics Tri- Directional Pattern (WGSTriDP): A Texture Feature Descriptor for Image Retrieval

The texture is a high-flying feature in an image and has been extracted to represent the image for image retrieval applications. Many texture features are being offered for image retrieval. This paper proposes a local binary pattern-based texture feature called Weber Global Statistics Tri-Directional Pattern (WGSTriDP) to retrieve the images. This pattern combines the advantages of differential excitation components in the Weber Local Binary Pattern (WLBP), sign and magnitude components in the Local Tri-Directional Pattern (LTriDP), and global statistics. Differential Excitation (DE) and Global Statistics TriDirectional Pattern (GSTriDP) are two components of WGSTriDP. The WGSTriDP gains the benefit of discrimination concerning human perception from differential excitation as well as incorporates global statistics into sign and magnitude components in the pattern derived from the local neighborhoods. The effectiveness of the pattern in image retrieval is experimented with in two benchmark databases, such as ORL (face database) and UIUC (texture database). According to the results of the experiments, WGSTriDP outperforms other local patterns in retrieving similar images from the database.

Automated diagnosis of pulmonary emphysema using multi-objective binary thresholding and hybrid classification

This paper is to estimate the potential of a deep learning method for automatic diagnosis of pulmonary emphysema. In the initial step, the dataset acquisition is performed by gathering a set of real-time dataset and the publicly available benchmark datasets known as the Computed Tomography Emphysema Database. After pre-processing of images, the lung segmentation is performed by the optimized binary thresholding. Here, the improvement of the segmentation is accomplished by the adoption of a hybrid meta-heuristic algorithm with Barnacles Mating Optimization (BMO), and Butterfly Optimization Algorithm (BOA) called Barnacles Mating-based Butterfly Optimization Algorithm (BM-BOA), in such a way to attain the multi-objective function concerning the variance and entropy of the image. Further, the feature descriptor called Weber Local Binary Pattern (WLBP) is used for generating the pattern image and the feature vectors. Two types of machine learning algorithms are used for the classification, in which Neural Network (NN) considers the feature vector from WLBP as input, and the deep learning model called Convolutional Neural Network (CNN) considers the WLBP pattern of the segmented image as input. In the hybrid classification model, the activation function is optimized by the same BM-BOA, which results in classifying the normal lung, mild emphysema, moderate (medium) emphysema, and severe emphysema. According to the experimental results with the comparison over the state-of-art-techniques, the proposed system permits inexpensive and reliable identification of emphysema on digital chest radiography.

Perceptual Image Hashing Based on Weber Local Binary Pattern and Color Angle Representation

This paper proposes an efficient scheme for generating image hashing by combining the local texture and color angle features. During the stage of texture extraction, using Weber's Law, the difference ratios between the center pixels and their surrounding pixels are calculated and the dimensions of these values are further reduced by applying principal component analysis to the statistical histogram. In the stage of color feature extraction, the color angle of each pixel is computed before dimensional reduction and is carried out using a discrete cosine transform and a significant coefficients selection strategy. The main contribution of this paper is a novel construction for image hashing that incorporates texture and color features by using Weber local binary pattern and color angular pattern. The experimental results demonstrate the efficacy of the proposed scheme, especially for the perceptual robustness against common content-preserving manipulations, such as the JPEG compression, Gaussian low-pass filtering, and image scaling. Based on the comparisons with the state-of-the-art schemes, receiver operating characteristic curves and integrated histograms of normalized distances show the superiority of our scheme in terms of robustness and discrimination.

Rotation-invariant Weber pattern and Gabor feature for fingerprint liveness detection

Fingerprint recognition systems are extensively deployed for the authentication in many applications. However, this kind of recognition systems may be spoofed by artificial fingerprints made from various materials. Thus, it is necessary to add a fingerprint liveness detection module to keep this kind of recognition systems on a good level of security. The fingerprint liveness detection (FLD) aims to judge whether a given fingerprint image is captured from a real finger or a spoof one. It is a typical two-class classification problem where the feature extraction is the key step. In this paper, we propose an effective feature extraction method for the FLD problem. The proposed features consist of two components, Weber local binary pattern (WLBP) and circularly symmetric Gabor feature (CSGF), analyzing the fingerprint images in both the spatial and frequency domains. The co-occurrence probabilities of the two components are calculated as the final features. The proposed features are utilized to train SVM classifiers separately on two databases in Fingerprint Liveness Detection Competition 2011 and 2013. Experimental results demonstrate the effectiveness of the proposed method.

Log-Gabor Weber descriptor for face recognition

It is well recognized that image representation is the most fundamental task of the face recognition, effective and efficient image feature extraction not only has small intraclass variations and large interclass similarity but also robust to the impact of pose, illumination, expression and occlusion. This paper proposes a new local image descriptor for face recognition, named Log–Gabor Weber descriptor (LGWD). The idea of LGWD is based on the image Log-Gabor wavelet representation and the Weber local binary pattern(WLBP) features. The main motivation of the LGWD is to enhance the multiple scales and orientations Log-Gabor magnitude and phase feature by applying the WLBP coding method. Histograms extracted from the encoded magnitude and phase images are concatenated into one to form the image description finally. The experimental results on the ORL, Yale and UMIST face database verify the representation ability of our proposed descriptor.

WLBP: Weber local binary pattern for local image description

In this paper, we propose a local descriptor, called Weber Local Binary Pattern (WLBP11WLBP is the abbreviation of Weber Local Binary Pattern. ), which effectively combines the advantages of WLD and LBP. Specifically, WLBP consists of two components: differential excitation and LBP. The differential excitation extracts perception features by Weber's law, while the LBP (Local Binary Pattern) can describe local features splendidly. By computing the two components, we obtain wo images: differential excitation image and LBP image, from which a WLBP histogram is constructed. The differential excitation was extended by bringing in Laplacian of Gaussian (LoG), which makes WLBP robust to noise. By designing a new quantization method, the discriminabilty of WLBP was enhanced. The proposed method is evaluated on the face recognition problem under different challenges. Experimental results show that WLBP performs better than WLD and LBP. Meanwhile, it is robust to time, facial expressions, lightings, pose and noise. We also conduct experiments on Brodatz and KTH-TIPS2-a texture databases, which demonstrate that WLBP is a powerful texture descriptor.