Local directional ZigZag pattern: A rotation invariant descriptor for texture classification

In this paper, local directional position pattern (LDPP) and local ternary pattern (LTP) are selected for facial recognition method which are having many advantages over previous techniques like local binary pattern (LBP) and local directional pattern (LDP). The selected techniques of LDPP and LTP are estrangement in their algorithms which help solely to extract features out of an image. LDPP is a revised form of LDP. In a typical LDP, only the top edge direction was taken into consideration, but strength sign of the pixel was not considered which may result in same code for opposite kind of edge pixel. This snag is overcome by LDPP which is further concatenated with LTP for better feature extraction. Once features are extracted they are trained using deep belief network. In the experimental work 10 images of each expression i.e. angry, surprise, disgust, neutral, sad, smile are selected. LDPP and LTP are concatenated followed by principal component analysis (PCA) and general discriminant analysis (GDA). Further for training, Deep Belief Network (DBN) is used which eventually increases the recognition rate and achieve accuracy of 95.3% which was 89.3% without concatenating.

Local line directional pattern for palmprint recognition

Face recognition and facial expression recognition using local patterns is the order of the day. Local directional number pattern (LDNP) is one of the prominent descriptor for face recognition. LDNP assigns a 3 bit code for each pixel in the image. The resultant LDNP labeled image is divided into regions to form histogram based descriptor. The histogram bins of all the regions are concatenated to form the final descriptor. In contrast to LDNP, a dimensionality reduced local directional number pattern (DR-LDNP) is proposed in this paper. The proposed descriptor computes single code for each block. This is done by X-ORing of the LDNP codes obtained in a single block. During the process, restructuring of the patterns is done by slightly modifying the LDNP coding pattern constraints. The resultant DR-LDNP descriptor outperforms the existing methods. The experimentation is carried out on standard databases like FERET, YALE, ORL, Cohn–Kannade and JAFFEE and obtained good recognition rates compared to other methods.

Weed identification techniques that are effective can lower the cost of weed control while also improving crop quality and output. Herbicides are currently the most frequent method of weed management. In order to deal with the issues of weed man-agement in agriculture, accurate crop and weed discrimination is essential. This work deals with the performance comparison of different classification algorithms namely Convolutional Neural Network(CNN), Graph Convolutional Networks(GCN), k-Nearest Neighbor(k-NN), and Support Vector Machine(SVM). Feature extraction algorithms, Local Directional Pattern(LDP) and Local Directional Relation Pattern(LDRP) are used alongside SVM and kNN. For data expansion, Generative Adversarial Network(GAN) was used, which generates synthetic samples of images from the dataset. The result of the study shows that CNN gives the maximum accuracy of 90.58% in ‘Crop and weed detection’ dataset when compared with other classification models used for investigation in our paper.

Local feature descriptor plays a significant role in texture classification. However, in the traditional local binary pattern method, image pixels are converted into a binary pattern based on the relationship between center and neighborhood pixels. This paper introduces a novel feature extraction method named local neighbourhood edge responsive binary pattern to extract and categorize reliable texture features from images for achieving an automated classification of different types of ocean bottom sediments. Initially, the basic magnitude variance pixel values are derived depending on an odd and even pixel value of a 3 × 3 image patch. Further, the edge information is extracted using the local directional pattern method from all images. The edge response of the image is obtained using a kirsch mask in all eight directions. The encoding condition is then applied to both the local intensity and the edge information to create a unique descriptor value. Finally, a new learning algorithm called GMJAYA-ELM combines the Gaussian mutated JAYA (GMJAYA) with an extreme learning machine (ELM) for texture classification. The GMJAYA is used to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks. Experimental findings indicate that the suggested method yields better accuracy and sensitivity efficiency among various groups. The proposed algorithm is tested by contrasting outcomes with standard learning schemes such as PSO-ELM, GA-ELM, ABC-ELM, Birdswarm-ELM, and JAYA-ELM, suggesting the dominance of GMJAYA-ELM.

In this paper we first present an effective image description method for facial expression recognition, which is a variation of local directional pattern (LDP). Then we introduce weightings on the modular’s LDP and investigate the effect on recognition rates with different weightings. Finally, the overlapped block is proposed when using LDP and proposed method. For recognition, this paper adopts PCA+LDP subspace method for feature reduction, and the nearest neighbor classifier is used in classification. The results of extensive experiments on benchmark datasets JAFFE and Cohn-Kanade illustrate that the proposed method not only can obtain better recognition rate but also have speed advantage. Moreover, the appropriately selected weightings and regional overlapping can improve recognition rates for both proposed method and LDP method.

The use of medical images by medical practitioners has increased to an extent that computers have become a necessity in the image processing and analysis. This research investigates if the Edge density and Local Directional Pattern can be used to characterize medical images. The performance of the Edge density and Local Directional Pattern features is assessed by finding their accuracy to retrieve images of the same group from a database. The combination of the Edge density and Local Directional Pattern features has shown to produce good results in both, classification of medical images and image retrieval. For the classification using the nearest neighbor and 5-nearest neighbor techniques yielded 98.2 % and 99.6 % classification success rates respectively and 99.4 % for image retrieval. The results achieved in this research work are comparable to other approaches used in literature.

Ear based identity recognition subject to uncontrolled conditions such as illumination changes, pose variation, low contrast, partial occlusion and noise, is an active research area in the field of biometrics. Meanwhile, multimodal biometrics is becoming increasingly popular and offers improved performance due to the use of multiple sources of information. In this paper, a multimodal biometrics system is proposed based on the ear and profile face that not only alleviates the short-comings of ear biometrics but also improves the overall recognition rate. The ear and profile face modalities are first represented individually using the combination of two efficient local feature descriptors namely, local phase quantization (LPQ) and local directional patterns (LDP). These histogram-based local descriptors are then combined into a high-dimensional feature vector that preserves complementary information in both frequency and spatial domains. The PCA along with the z-score normalization technique is independently applied on each feature vector and the resultant reduced feature vectors are combined at the feature level. The kernel discriminative common vector (KDCV) approach is finally exploited over the combined feature set to derive more discriminative and non-linear features for the identification of individuals using kNN classifier. The effectiveness of the proposed model has been verified with the deep features derived from three popular pre-trained CNN models such as AlexNet, VGG16 and GoogleNet. Experimental results on two benchmark databases clearly show that the proposed approach achieves better performance than individual modality and other state-of-the-art methods.

Texture classification generally requires the analysis of patterns in local pixel neighborhoods. Statistically, the underlying processes are comprehensively described by their joint probability density functions (jPDFs). Even for small neighborhoods, however, stable estimation of jPDFs by joint histograms (jHSTs) is often infeasible, since the number of entries in the jHST exceeds by far the number of pixels in a typical texture region. Moreover, evaluation of distance functions between jHSTs is often computationally prohibitive. Practically, the number of entries in a jHST is therefore reduced by considering only two-pixel patterns, leading to 2D-jHSTs known as cooccurrence matrices, or by quantization of the gray levels in local patterns to only two gray levels, yielding local binary patterns (LBPs). Both approaches result in a loss of information. We introduce here a framework for supervised texture classification which reduces or avoids this information loss. Local texture neighborhoods are first filtered by a filter bank. Without further quantization, the jPDF of the filter responses is then described parametrically by gaussian mixture models (GMMs). We show that the parameters of the GMMs can be reliably estimated from small image regions. Moreover, distances between the thus modelled jPDFs of different texture patterns can be computed efficiently in closed form from their model parameters. We furthermore extend this texture descriptor to achieve full invariance to rotation. We evaluate the framework for different filter banks on the Brodatz texture set. We first show that combining the LBP difference filters with the GMM-based density estimator outperforms the classical LBP approach and its codebook extensions. When replacing these-rather elementary-difference filters by the wavelet frame transform (WFT), the performance of the framework on all 111 Brodatz textures exceeds the one obtained more recently by spin image and RIFT descriptors by Lazebnik et al.

Recently, there has been a huge demand for assistive technology for industrial, commercial, automobile, and societal applications. In some of these applications, there is a requirement of an efficient and accurate system for automatic facial expression recognition (FER). Therefore, FER has gained enormous interest among computer vision researchers. Although there has been a plethora of work available in the literature, an automatic FER system has not yet reached the desired level of robustness and performance. In most of these works, there has been the dominance of appearance-based methods primarily consisting of local binary pattern (LBP), local directional pattern (LDP), local ternary pattern (LTP), gradient local ternary pattern (GLTP), and improved local ternary pattern (IGLTP). Keeping in view the popularity of appearance-based methods, in this paper, we have proposed an appearance-based descriptor called Improved Adaptive Local Ternary Pattern (IALTP) for automatic FER. This new descriptor is an improved version of ALTP, which has been proved to be effective in face recognition. We have investigated ALTP in more details and have proposed some improvements like the use of uniform patterns and dimensionality reduction via principal component analysis (PCA). The reduced features are then classified using kernel extreme learning machine (K-ELM) classifier. In order to validate the performance of the proposed method, experiments have been conducted on three different FER datasets using well-known evaluation measures such as accuracy, precision, recall, and F1-Score. The proposed approach has also been compared with some of the state-of-the-art works in literature and found to be more accurate and efficient.

Extraction of human emotions from facial expression has attracted significant attention in computer vision community. There are several appearance based techniques like local binary patterns (LBP), local directional patterns (LDP), local ternary patterns (LTP) and gradient local ternary patterns (GLTP). Recently, many investigations have been done to improve these feature extraction techniques. Although GLTP has achieved an improvement in robustness to noise and illumination, it encodes image gradient in four directions and two orientations only. This paper proposes to improve GLTP to directional gradient local ternary patterns (DGLTP) by encoding image gradient on eight directions and four orientations. The eight directional Kirsch mask is used to encode the image gradient followed by dimensionality reduction using linear discriminant analysis (LDA) and AVG, MAX and MIN pooling techniques are compared for fusing facial expression features. The proposed technique was experimented on JAFFE facial expression dataset with support vector machine (SVM). The experimental results show that proposed technique improved accuracy of GLTP.

This paper represents automatic facial expression analysis method named Local Directional Stigma Mean Patterns (LDSMP) for automatic facial expression analysis and image retrieval using content based facial expression image retrieval and CNN. The traditional local patterns such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) are applied for face recognition and expression analysis, calculated using relationship between the center pixel and neighboring pixels. The proposed method calculates the eight directional difference values then divided into the three ranges based on threshold values. Thus, the values are substituted with basic three positive values (+3, +2, +1) and three negative values (-3, -2, -1) to get more sensitive information from an image rather than aforementioned methods. The threshold can be select either static which is selected by user or dynamic is evaluated from image itself and supports to improve the efficiency. The performance of the proposed method is further improved by giving this patterns as input to the Convolutional Neural Networks (CNN) and compared with the existing methods LBP, LTP, and Directional Binary Code (DBC) in terms of Average Precision (AP), Average Recall (AR), and Average Retrieval Rate (ARR) using standard databases COREL 10K (DB1) and JAFFE (The Japanese Female Facial Expression) (DB2) and Extended Cohn-Kanade (CK +) (DB3) dataset.

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

Facial expression recognition is an important part of the study in man-machine interaction. Principal component analysis (PCA) is an extraction method based on statistical features which were extracted from the global grayscale features of the whole image .But the grayscale global features are environmentally sensitive. In order to recognize facial expression accurately, a fused method of principal component analysis and local direction pattern (LDP) is introduced in this paper. First, PCA extracts the global features of the whole grayscale image; LDP extracts the local grayscale texture features of the mouth and eyes region, which contribute most to facial expression recognition, to complement the global grayscale features of PCA. Then we adopt Support Vector Machine (SVM) classifier for expression classification. Experimental results demonstrate that this method can classify different expressions more effectively and get higher recognition rate compared with the traditional method.

This paper presents a novel local feature descriptor, the Local Directional Pattern (LDP), for describing local image feature. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy situation. A rotation invariant LDP code is also introduced which uses the direction of the most prominent edge response. Finally an image descriptor is formed to describe the image (or image region) by accumulating the occurrence of LDP feature over the whole input image (or image region). Experimental results on the Brodatz texture database show that LDP impressively outperforms the other commonly used dense descriptors (e.g.,Gabor-wavelet and LBP).