Local Tetra Pattern Research Articles

Enhanced ResNet-151-based fused features for optimized Bi-LSTM-DNN-aided handwritten character and digits recognition

Optical Character Recognition (OCR) is a method to convert a scanned photo of“handwritten character recognition (HCR) or printed character recognition (PCR)” into a form of digital text. HCR is a version of OCR, which is remarkably modeled to identify handwritten text, while PCR aims at printed text identification. The identification of handwritten characters and digits is more complicated as compared to PCR because of the diversities in human writing styles, stokes, thickness, and curves of characters. Similarly, achievements in several computer vision tasks consider the Convolutional Neural Networks (CNN) to give an end-to-end solution for HCR with huge success. However, the process of significant feature learning for the identification of images is complicated with little data. Hence, this paper aims to develop a new handwritten character and digit recognition model with the incorporation of a deep learning strategy. Initially, the data related to Indian languages are collected from the standard benchmark datasets. Then, the collected data are given into the feature extraction phase 1, where the ResNet 151 is used for extracting the feature set 1. Similarly, the data gathered are considered in the feature extraction phase 2, where the Optimal Ensemble Pattern extraction approach is developed with Local Binary Pattern (LBP), Local Gradient Patterns (LGP), Local Tetra Pattern (LTrP), and Local Vector Pattern (LVP) for extracting the significant patterns from the language data. These extracted patterns are given into the ResNet 151 for getting the feature set 2. Here, the features from ResNet 151 get optimized with the enhanced optimization algorithm with Fitness-based Sail Fish Optimizer (F-SFO). The obtained feature set 1 and optimal feature set 2 are concatenated for performing final recognition. At last, the HCR is done with the help of developed Bi-LSTM-DNN to achieve the enhanced and accurate recognition of handwritten characters of the Indian languages. The performances of character recognition are further improved with the parameter optimization in Bi-LSTM-DNN with the same enhanced F-SFO. Overall result analysis, the accuracy of the designed method attains 95.12%.

An implementation of intelligent YOLOv3-based anomaly detection model from crowded video scenarios with optimized ensemble pattern extraction

ABSTRACT The anomaly or abnormality detection in crowded scenes helps in identifying the violence and protecting the people from severe damage. Thus, there is a need to detect the anomalies with the classifier for learning information along with the usage of huge architectures. A new anomaly detection model is implemented in this model. The collected data is fed to optimal ensemble pattern extraction scheme through techniques like Local binary patterns (LBP), Local Gradient Pattern (LGP), and Local Tetra Pattern (LTrP). The weights are tuned by a new hybrid Spiral Search-based Black Widow Glowworm Swarm Optimization (SS-BWGSO) for getting the optimal ensemble patterns. Next, anomaly frame classification is carried out by optimized VGG16+ResNet technique, where the hyperparameters of VGG16 and ResNet are tuned by SS-BWGSO algorithm. Finally, anomaly detection is performed by the YOLOV3 classifier. Throughout the result analysis the higher performance of the designed technique is observed over the classical methods.

A Robust Triangular Sigmoid Pattern-Based Obstacle Detection Algorithm in Resource-Limited Devices

Object detection and classification are key processes in advanced driver-assistance systems. The existing object detection and classification methods are effective in normal daylight conditions. However, the performance of these methods deteriorates in adverse driving conditions, such as those involving low light, illumination changes, and nighttime conditions. To overcome these limitations, several feature-based algorithms have been developed that introduce local features, such as local binary pattern, local tetra pattern, and local density encoding, for adverse driving conditions. However, these local patterns cannot effectively address the noise in real driving conditions because the relationship between the neighboring pixels cannot be comprehensively encoded. To solve these problems, this study developed a robust feature-based method by introducing a triangular-pattern-based sigmoid function to effectively encode and establish the robust feature of neighboring pixels in the local region. The performance of the proposed pattern is evaluated by integrating it into state-of-the-art object detection algorithms. The proposed method significantly increases the vehicle detection ratio of YOLOv5s by 11.7% for an intersection over a union of 0.5 in difficult driving conditions for the CCD dataset. Moreover, the detection ratios of the proposed method are comparable to those of other state-of-the-art object detection methods such as Retina, Faster RCNN, and Deformable DETR over various datasets such as KITTI, COCO, HCI, and CCD. Additionally, the proposed algorithm is implemented on a Raspberry Pi-based autonomous car system to evaluate its performance during real driving conditions. Our proposed method supports robust input feature extraction and can thus be used to enhance the performance of the existing obstacle detection and classification systems.

Image classification using gaussian curvature based local tetra pattern descriptor with enhanced feature selection technique

A Gaussian Curvature-based Local Tetra Descriptor (GCLTrP) is proposed in this paper to incorporate geometric discriminative feature extraction using a hybrid combination of Gaussian Curvature (GC) and Local Terta Pattern (LTrP). The texture of an image is locally discriminant, capturing the equivalent binary response from the gaussian curvature. The extracted feature value is fed into the Enhanced Grey Wolf Optimization (EGWO), a lightweight metaheuristic searching algorithm that selects the best optimal textural features. The proposed GCLTrP with EGWO method’s effective performance is validated using the benchmarks dataset, and the results are tested using the performance evaluation metric. In comparison to other cutting-edge methods, the proposed method achieves the highest overall classification accuracy of 100% on the Brodatz and RS datasets. In terms of computational redundancy and noise reduction, the proposed technique outperforms the other existing techniques.

Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches

An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures.

Optimization assisted convolutional neural network for detection of thyroid

SummaryThis research work endeavor to suggest a new thyroid detection system that involves three main processes like segmentation, feature extraction, and detection. Initially, for the segmentation process, an improved watershed algorithm is proposed. Besides, from the segmented image, it extracted the texture features that comprises local binary patterns, modified GLCM feature, local tetra pattern features, together with the statistical features. Primarily, the extracted features are given as an input to convolutional neural network (CNN) for the final detection result. For making the detection more accurate, the training of deep CNN is performed via a new deer encircling included gray wolf optimization model for optimal weights tuning. The adopted hybrid algorithm combines gray wolf optimization and deer hunting optimization algorithm. At last, the outcomes of the adopted scheme is validated with the extant approaches under various metrics.

Optimization assisted framework for thyroid detection and classification: A new ensemble technique

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like “(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification”. Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as“ Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)” are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of “curse of dimensionality”. Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.

Infrared Small Target Detection Using Local Feature-Based Density Peaks Searching

In this letter, we propose a new local feature-based method for the detection of infrared small targets with the density feature map that can effectively suppress noise in the feature domain. First, we combine the local tetra pattern (LTrP) and the second-order LTrP to generate the density feature map. Second, we apply density peaks searching to the feature map to obtain candidate targets. Third, we generate two local features, i.e., the entropy and third-order moment, for each image patch whose center is a candidate target and then fuse them to employ the fused feature to find the real target. The experimental results demonstrate that our proposed method achieves better performance compared with the state-of-the-art approaches.

A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

<p class="0abstract">Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.</p>

Improved Content Based Image Retrieval Process Based on Deep Convolutional Neural Network and Salp Swarm Algorithm

Digital image and medical image retrieval from several repositories are improving gradually, so the capacity of repositories increases rapidly. The semantic space is the main issue on content-based image retrieval (CBIR), which exists among the semantic level as well as increases the data recognized through human and low level visible data obtained through the image. The CBIR system utilizes the deep convolutional neural network (DCNN), which is trained to medical image characterization and the digital image by salp swarm optimization algorithm (SSA). The average classification accuracy for medical image is 86.805%, a mean average precision is 79%, Average Recall Rate (ARR) is 91.7% and [Formula: see text]-measure is 84.9%, are achieved during retrieval task. For image retrieval, the Average Precision Rate (APR) improved from 39%, 40%, 36% and 42.5% to 86.8% and the ARR enhanced from 39.5%, 40.5%, 35.5% and 42.5% to 86.8%. The [Formula: see text]-measure is improved from 39.5%, 40.5%, 35.5% and 42.5% to 86.8% as different with Local tetra patterns (LTrP), LOOP, local derivative pattern (LDP) and local mean differential excitation pattern (LMDeP) separately on Corel-1K dataset. The presented method is most suitable for multimodal digital images and medical image retrieval for various parts of the body.

Hybrid classification with meta‐heuristic‐enabled optimal feature selection for thyroid detection

AbstractThyroid is a widespread disease, affecting most victims. The diagnosis of thyroid remains a complex process, as its detection in patients is highly intricate. Hence, the doctors are needed to be aware of the risk factors and symptoms of the disease. This paper aims to propose a novel thyroid diagnosis scheme, involving three major phases: (a) feature extraction, (b) optimal feature selection, and (c) classification. Initially, the thyroid image and the related data serve as input for diagnosing the disease. In the first phase, the features like, gray level co‐occurrence matrix (GLCM), gray level run length matrix (GLRM), local binary pattern (LBP), local vector pattern (LVP), and local tetra patterns (LTrP) are extracted from the input image. Additionally, the features from data are extracted using Principal Component Analysis (PCA) for resolving the issue of “curse of dimensionality.” The optimal features are then selected using a hybrid optimization approach. The optimally selected features of the image and the data are then subjected to the classification process via convolutional neural network (CNN) and neural network (NN), respectively. Both the classified outputs undergo “AND” binary operation to yield the final classified output. To yield effective classification, the NN model is trained by tuning its weights using the proposed algorithm. Further, this paper introduces a new hybrid algorithm, termed firefly updated lion optimization (SLnO) algorithm (FU‐SLnO), for attaining optimal outcomes. Finally, the efficiency of the proposed work is compared over few other conventional approaches and its superiority is proven.

An efficient facial emotion recognition system using novel deep learning neural network-regression activation classifier

In the computer vision field, FER encompasses a significant place. It is being studied for a long period, and in recent decades, it has attained progress, but all is in vain, since, recognizing facial expression with high accuracy is still hard due to disparate facial expressions. To beat such difficulties, an efficient Facial Emotion Recognitions (FER) is proposed by utilizing a novel Deep Learning Neural Network-regression activation (DR) classifier. The proposed method has six phases, namely, pre-processing, facial point extraction, segmentation, feature extraction, feature selection, and classification. Initially the input image has been pre-processed using Gamma-HE technique and then facial points are extracted using Pyramid Histogram of Oriented Gradients (PHOG) based Supervised Descent (SMD) Method. The facial parts are segmented using Viola-Jones Algorithm (VJA) and then Local Tetra Pattern (LTrP), cluster shade, Inverse Divergent Moment (IDM), Local homogeneity, optimum probability, cluster prominence, dissimilarity, autocorrelation, and contrast features have been extracted. Modified Monarch Butterfly Optimization (MMBO) algorithm has been used to select necessary features from the extracted features. From the extracted facial points, the DR classifier classifies the emotions of the particular input image. A ‘2’ datasets were taken for analyzing the proposed system’s performance. Centred on the CK+ database, the proposed work attains 0.9885-accuracy, and centred on the JAFFE database, it has 0.9727-accuracy. Also, the investigational results proved that the proposed work trounces the existing systems centred on statistical metrics.

Diabetic Retinopathy Detection Using Optimization Assisted Deep Learning Model: Outlook on Improved Grey Wolf Algorithm

In recent days, study on retinal image remains a significant area for analysis. Several retinal diseases are identified by examining the differences occurring in the retina. Anyhow, the major shortcoming between these analyses was that the identification accuracy is not satisfactory. The adopted framework includes two phases namely; (i) feature extraction and (ii) classification. Initially, the input fundus image is subjected to the feature extraction process, where the features like Local Binary Pattern (LBP), Local Vector Pattern (LVP) and Local Tetra Patterns (LTrP) are extracted. These extracted features are subjected to the classification process, where the Deep Belief Network (DBN) is used as the classifier. In addition, to improve the accuracy, the activation function and hidden neurons of DBN are optimally tuned by means of the Self Improved Grey Wolf Optimization (SI-GWO). Finally, the performance of implemented work is compared and proved over the conventional models.

A Harmony Search-Based Wrapper-Filter Feature Selection Approach for Microstructural Image Classification

Besides chemical composition, microstructure plays a key role to control the properties of engineering materials. A strong correlation exists between microstructure and many mechanical and physical properties of a metal. It has the utmost importance to understand the microstructure and distinguish the microstructure accurately for the appropriate selection of engineering materials in product fabrication. Computer vision and machine learning play a major role to extract the feature and predict the most probable class of a 7-class microstructural image with a high degree of accuracy. Features contain information about the image, and the classification function is defined in terms of features. Feature selection plays an important role in the classification problem to improve the classification accuracy and also to reduce the computational time by eliminating redundant or non-influential features. The current research aims at classifying microstructure image datasets by an improved wrapper-filter based feature selection method using texture-based feature descriptor. Before applying the feature selection method, a feature descriptor, called rotational local tetra pattern (RLTrP), is applied to extract the features from the input images. Then, an ensemble of three filter methods is developed by considering the union of the top-n features selected by Chi-square, Fisher score, and Gini impurity-based filter methods. The objective of this ensemble is to combine all possible important features selected by three filter methods which will be used to create an initial population of the wrapper-based meta-heuristic feature selection algorithm called, harmony search (HS). The novelty of this HS method lies in the objective function, which is defined as a function of Pearson correlation coefficient and mutual information to calculate the fitness value. The proposed method not only optimizes features with reduced dimension but also improves the performance of classification accuracy of the 7-class microstructural images. Moreover, the proposed HS model has also been compared with some standard optimization algorithms like whale optimization algorithm, particle swarm optimization, and Grey wolf optimization on the present dataset, and in every case, the HS method ensures better agreement between feature selection and classification accuracy than the other methods.

Image Authenticity Detection Using DWT and Circular Block-Based LTrP Features

Copy-move forgery is the most common type of digital image manipulation, in which the content from the same image is used to forge it. Such manipulations are performed to hide the desired information. Therefore, forgery detection methods are required to identify forged areas. We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern (LTrP) features to detect the single and multiple copy-move attacks from the images. The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations. It also uses discrete wavelet transform (DWT) for dimension reduction. The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods. Finally, Jeffreys and Matusita distance is used for similarity measurement. For the evaluation of the results, three datasets are used, namely MICC-F220, MICC-F2000, and CoMoFoD. Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.

Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

Texture image retrieval using hybrid directional Extrema pattern

In this paper, a new descriptor Hybrid Directional Extrema Pattern (HDEP) for the retrieval of texture images by integrating the concept of Weight Difference Directional Local Extrema Pattern (WD_DLEP) and Directional Local Extrema Pattern (DLEP) is proposed. The texture patterns are computed for four principle directions i.e., 0°, 45°, 90°, 135°. The proposed approach considers the difference between central pixel and corresponding neighboring pixels in the specified directions. This difference is used as weight in next stage. This weight is compared with a user-defined threshold to determine the value of strong bits in a feature vector. Experimental evaluation on three benchmark datasets (Brodatz, VisTex and Describable Textures Dataset) illustrates the better performance of proposed system with the other state-of-the-art techniques on two basic parameters i.e., average retrieval rate and time. The proposed approach is evaluated against various state-of-the-art texture image-retrieval systems based on local binary pattern, directional local extrema pattern, local tetra pattern, block-based local binary pattern, center-symmetric local binary pattern and wavelet. Significant improvement has been achieved in image retrieval performance due to assignment of weight in the pattern generation process. Further, the proposed approach is capable of differentiating different texture patterns more efficiently because it uses magnitude of pixel differences to determine the value of current pixel rather than the sign of pixel differences.

Combined global and local semantic feature–based image retrieval analysis with interactive feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

An Exhaustive Multi Factor Face Authentication Using Neuro-Fuzzy Approach

The face authentication is a challenging task to validate the user with uncontrolled environment like variations on expression, pose, illumination and occlusion. In order to address these issues, the proposed work provides solution by considering all these factors in inter and intra personal face authentication. During enrollment process, the facial region of still image for the authorized user is detected and features are extracted using local tetra pattern (LTrP) technique. The features are given as input to the neural network namely fuzzy adaptive learning control network (FALCON) for training and classification of features. During authentication process, an image that can vary with expression, pose, illumination and occlusion factors is taken as test image and the test image is applied with LTrP and FALCON to train the features of test image. Then, these trained features are compared with existing feature set by using new proposed multi factor face authentication algorithm to authenticate a person. This work is evaluated among 1150 face images which are collected from JAFFE, Yale, ORL and AR datasets. The overall performance of the work is evaluated by authenticating 1106 images from 1150 constrained images. The second phase of the research work finally produces highest recognition rate of 96% among conventional methods.