Improved Local Binary Pattern Research Articles

Dynamic mutation based glowworm swarm optimization with long short-term memory approaches for thyroid nodule classification

Objectives: To design an efficient approach for thyroid nodule classification with higher true positive rate. Methodology and statistical analysis: The proposed system designed as a Dynamic Mutation based Glowworm Swarm Optimization with Long-Short Term Memory (DMGSO with LSTM) scheme for thyroid nodule classification. In this proposed research work, input thyroid images are preprocessed by using Dynamically Weighted Median Filter (DWMF). The preprocessed images are segmented with the help of Region based Active Contour scheme. An Improved Local Binary Pattern (ILBP), Grey Level Cooccurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOG) features are extracted from segmented image. Then the optimal features are selected by using Dynamic Mutation based Glowworm Swarm Optimization (DMGSO) algorithm. Finally, the Long-Short Term Memory (LSTM) scheme is utilized for classifying the thyroid nodule. Findings: The experimental results show that the proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall and f-measure. Keywords: Thyroid nodule; Histogram of Oriented Gradient (HOG); Long Short-Term Memory (LSTM); Dynamic Mutation based Glow worm Swarm Optimization (DMGSO)

Local texture descriptors and projection histogram based handwritten Meitei Mayek character recognition

In this paper, we use the local texture descriptor and projection histogram feature for handwritten Meitei Mayek (Manipuri script) recognition. The different variations of the local binary pattern (LBP) namely, uniform LBP (ULBP), improved LBP (ILBP), center-symmetric LBP (CS-LBP) account for local texture descriptors. These features along with the projection histogram, separately and combined are presented to machine learning algorithms, k nearest neighbor (KNN), support vector machine (SVM) and Random Forest (RF) for classification of characters. The experiments by these feature descriptors with the classifiers have been evaluated on self-collected handwritten Meitei Mayek character dataset having 9800 samples. High accuracy is achieved even with the simple KNN classifier. Furthermore, classification with SVM and RF are explored, and the results are compared with the pixel-based methods which use the intensities value directly and a classic CNN model for recognition. The comparative results show that local texture descriptors and projection histogram strongly outperform pixel-based methods. The overall superior accuracy is achieved when the feature descriptors are combined with KNN classifier and performed even better than the CNN model.

Support Vector Machine Optimized Using the Improved Fish Swarm Optimization Algorithm and Its Application to Face Recognition

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

Improved opponent color local binary patterns: an effective local image descriptor for color texture classification

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images.

Improved multispectral aggregate channel for pedestrian detection

To solve the problem of low pedestrian detection accuracy in a single band due to variability and complexity of external environment, an improved pedestrian detection algorithm based on multispectral aggregate channel feature is proposed. The aggregate channel features of visible images and infrared images are extracted, respectively. The pixel contrast rule is changed and the results are compared with the adaptive threshold. The im-proved central symmetric local binary pattern feature is added to the feature channels. Different filter banks are designed to filter the multispectral aggregate channel features. The classifier is trained to realize the multispectral pedestrian detection. Experiments show that the improved local binary pattern feature can describe the symmetry of pedestrians of infrared images better and the intermediate filter layer enriches the candidate feature pool. The algorithm can effectively detect pedestrians in various scenes and improve the pedestrian detection accuracy. Compared with the previous multispectral aggregate channel detection work, the algorithm reduces the log-average miss rate.

Improved local binary pattern for real scene optical character recognition

A strong edge descriptor is an important topic in a wide range of applications. Local binary pattern (LBP) techniques have been applied to numerous fields and are invariant with respect to luminance and rotation. However, the performance of LBP for optical character recognition is not as good as expected. In this study, we propose a robust edge descriptor called improved LBP (ILBP), which is designed for optical character recognition. ILBP overcomes the noise problems observed in the original LBP by searching over scale space, which is implemented using an integral image with a reduced number of features to achieve recognition speed. In experiments, we evaluated ILBP's performance on the ICDAR03, chars74K, IIIT5K, and Bib digital databases. The results show that ILBP is more robust to blur and noise than LBP.