Robust Texture Classification Using Local Correlation Features for Thai Buddha Amulet Recognition

2015 ◽  
Vol 781 ◽  
pp. 531-534
Author(s):  
Weera Kompreyarat ◽  
Thanasin Bunnam
Keyword(s):  
Texture Classification ◽  
Recognition Rate ◽  
Training Data ◽  
Controlled Environment ◽  
Background Color ◽  
Local Correlation ◽  
Computation Complexity ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Technique

In this paper, we propose a development of Thai Buddha amulet identification using simple local correlation features. By using this technique, it has an ability to deal with variety of the amulet materials and colors in the same generation with less computation complexity. Moreover, it is able to apply for semi-controlled environment, which states that the image just has a plain background color that different from the amulet one. This article uses K-nearest neighbors as classification technique. The experiment was done automatically by using amulet images from the internet, which ensured that each image in the same class had a different in light intensity, contrast and color. There were 240 images with 80 classes for training data set and 751 images for test data set. The result shows that the proposed method gains a high recognition rate about 89.35%.

Hand gesture recognition via image processing techniques and deep CNN

2020 ◽  
Vol 39 (3) ◽  
pp. 4405-4418
Author(s):  
Yao-Liang Chung ◽  
Hung-Yuan Chung ◽  
Wei-Feng Tsai
Keyword(s):  
Skin Color ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Training Data ◽  
Background Information ◽  
Hand Gestures ◽  
Data Set ◽  
Practical Applications ◽  
Deep Cnn ◽  
Hand Region

In the present study, we sought to enable instant tracking of the hand region as a region of interest (ROI) within the image range of a webcam, while also identifying specific hand gestures to facilitate the control of home appliances in smart homes or issuing of commands to human-computer interaction fields. To accomplish this objective, we first applied skin color detection and noise processing to remove unnecessary background information from the captured image, before applying background subtraction for detection of the ROI. Then, to prevent background objects or noise from influencing the ROI, we utilized the kernelized correlation filters (KCF) algorithm to implement tracking of the detected ROI. Next, the size of the ROI image was resized to 100×120 and input into a deep convolutional neural network (CNN) to enable the identification of various hand gestures. In the present study, two deep CNN architectures modified from the AlexNet CNN and VGGNet CNN, respectively, were developed by substantially reducing the number of network parameters used and appropriately adjusting internal network configuration settings. Then, the tracking and recognition process described above was continuously repeated to achieve immediate effect, with the execution of the system continuing until the hand is removed from the camera range. The results indicated excellent performance by both of the proposed deep CNN architectures. In particular, the modified version of the VGGNet CNN achieved better performance with a recognition rate of 99.90% for the utilized training data set and a recognition rate of 95.61% for the utilized test data set, which indicate the good feasibility of the system for practical applications.

scholarly journals An Overlapping Cell Image Synthesis Method for Imbalance Data

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yi Ning Xie ◽  
Lian Yu ◽  
Guo Hui Guan ◽  
Yong Jun He
Keyword(s):  
Dna Ploidy ◽  
Sample Selection ◽  
Recognition Rate ◽  
Synthesis Method ◽  
Image Synthesis ◽  
Training Data ◽  
Data Set ◽  
Ploidy Analysis ◽  
Imbalance Problem ◽  
Imbalanced Training Data

DNA ploidy analysis of cells is an automation technique applied in pathological diagnosis. It is important for this technique to classify various nuclei images accurately. However, the lack of overlapping nuclei images in training data (imbalanced training data) results in low recognition rates of overlapping nuclei images. To solve this problem, a new method which synthesizes overlapping nuclei images with single-nuclei images is proposed. Firstly, sample selection is employed to make the synthesized samples representative. Secondly, random functions are used to control the rotation angles of the nucleus and the distance between the centroids of the nucleus, increasing the sample diversity. Then, the Lambert-Beer law is applied to reassign the pixels of overlapping parts, thus making the synthesized samples quite close to the real ones. Finally, all synthesized samples are added to the training sets for classifier training. The experimental results show that images synthesized by this method can solve the data set imbalance problem and improve the recognition rate of DNA ploidy analysis systems.

Classification of Single Cereal Grain Kernel Using Shape Parameters Based on Machine Vision

2012 ◽  
Vol 605-607 ◽  
pp. 2179-2182 ◽  
Author(s):  
Lan Lan Wu ◽  
Jie Wu ◽  
You Xian Wen ◽  
Li Rong Xiong ◽  
Yu Zheng
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Recognition Rate ◽  
Bayesian Classifier ◽  
Training Data ◽  
Japonica Rice ◽  
Shape Feature ◽  
Data Set ◽  
Cereal Grain ◽  
Length Width

The study was conducted to identify three types of non-touching grain kernels using a colour machine vision system. Images of individual cereal grain kernels were acquired using an camera. Shape feature was extracted from binary and edge images of cereal grain kernels obtained by iamge processing for classification. A total of 13 shape feature parameters, including region area, perimeter, length, width, the maximum radius, the smallest radius etc, were extracted from each kernel to use as input to the Bayesian classifier. Experimental results showed that the Bayesian classifier gave better classification with a calssificaiton accuracy of 99.67% for indica type rice, followed by 98.67% and 78.33% for japonica rice and glutinous rice using training set, respectively. The classification system was developed with Bayesian classifier that achieved an overall recognition rate of 92.22% with training data set and furthermore, a classification accuracy of 90% for the testing data set.

scholarly journals Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mengmeng Huang ◽  
Fang Liu ◽  
Xianfa Meng
Keyword(s):  
Target Recognition ◽  
Recognition Rate ◽  
Automatic Target Recognition ◽  
Small Sample ◽  
Training Data ◽  
Small Scale ◽  
Data Set ◽  
Shape Information ◽  
Target Shape ◽  
The One

Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set.

scholarly journals Real-Time Indonesian Language Speech Recognition with MFCC Algorithms and Python-Based SVM

2019 ◽  
Vol 3 (2) ◽  
pp. 55
Author(s):  
Wening Mustikarini ◽  
Risanuri Hidayat ◽  
Agus Bejo
Keyword(s):  
Speech Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
Human Voice ◽  
Average Accuracy ◽  
Speech Data ◽  
Mel Frequency Cepstral Coefficient

Abstract — Automatic Speech Recognition (ASR) is a technology that uses machines to process and recognize human voice. One way to increase recognition rate is to use a model of language you want to recognize. In this paper, a speech recognition application is introduced to recognize words "atas" (up), "bawah" (down), "kanan" (right), and "kiri" (left). This research used 400 samples of speech data, 75 samples from each word for training data and 25 samples for each word for test data. This speech recognition system was designed using Mel Frequency Cepstral Coefficient (MFCC) as many as 13 coefficients as features and Support Vector Machine (SVM) as identifiers. The system was tested with linear kernels and RBF, various cost values, and three sample sizes (n = 25, 75, 50). The best average accuracy value was obtained from SVM using linear kernels, a cost value of 100 and a data set consisted of 75 samples from each class. During the training phase, the system showed a f1-score (trade-off value between precision and recall) of 80% for the word "atas", 86% for the word "bawah", 81% for the word "kanan", and 100% for the word "kiri". Whereas by using 25 new samples per class for system testing phase, the f1-score was 76% for the "atas" class, 54% for the "bawah" class, 44% for the "kanan" class, and 100% for the "kiri" class.

scholarly journals Social Media Data using Various Classification Algorithms in Datamaning

2019 ◽  
Vol 8 (12S) ◽  
pp. 588-589
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Training Data ◽  
Classification Algorithms ◽  
Data Set ◽  
Social Media Data ◽  
The Past ◽  
Classification Technique ◽  
Mathematical Techniques ◽  
Media Data

Data Mining is one of the most successful domains in research. It describes the past and speculates the future for analysis. There are several techniques used in data mining. Among them classification is one of the main data mining techniques based on machine learning. In classification technique data set is classified into predefined set of groups or classes. Mathematical techniques such as decision tree, linear regression, neural networks and statistics are used for classification methods. Classification is a problem to identify which set of categories the new observation belongs to using training data set. This paper analyses the data taken from social media and uses the classification algorithm for making a comparative study on social advertisement using python.

scholarly journals Detection of Smoking in Indoor Environment Using Machine Learning

2020 ◽  
Vol 10 (24) ◽  
pp. 8912
Author(s):  
Jae Hyuk Cho
Keyword(s):  
Machine Learning ◽  
Indoor Air ◽  
Indoor Environment ◽  
Indoor Air Pollution ◽  
Binary Classification ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Indoor Smoking

Revealed by the effect of indoor pollutants on the human body, indoor air quality management is increasing. In particular, indoor smoking is one of the common sources of indoor air pollution, and its harmfulness has been well studied. Accordingly, the regulation of indoor smoking is emerging all over the world. Technical approaches are also being carried out to regulate indoor smoking, but research is focused on detection hardware. This study includes analytical and machine learning approach of cigarette detection by detecting typical gases (total volatile organic compounds, CO2 etc.) being collected from IoT sensors. In detail, data set for machine learning was built using IoT sensors, including training data set securely collected from the rotary smoking machine and test data set gained from actual indoor environment with spontaneous smokers. The prediction accuracy was evaluated with accuracy, precision, and recall. As a result, the non-linear support vector machine (SVM) model showed the best performance with 93% in accuracy and 88% in the F1 score. The supervised learning k-nearest neighbors (KNN) and multilayer perceptron (MLP) models also showed relatively fine results, but shows effectivity simplifying prediction with binary classification to improve accuracy and speed.

scholarly journals The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

2021 ◽  
Vol 14 (2) ◽  
pp. 120-128
Author(s):  
Mohammed Ehsan Safi ◽  
Eyad I. Abbas
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Training Data ◽  
Data Sets ◽  
Training Set ◽  
Data Set ◽  
Person Recognition ◽  
Personal Recognition ◽  
Application Requirements ◽  
Training Sets

In personal image recognition algorithms, two effective factors govern the system’s evaluation, recognition rate and size of the database. Unfortunately, the recognition rate proportional to the increase in training sets. Consequently, that increases the processing time and memory limitation problems. This paper’s main goal was to present a robust algorithm with minimum data sets and a high recognition rate. Images for ten persons were chosen as a database, nine images for each individual as the full version of the training data set, and one image for each person out of the training set as a test pattern before the database reduction procedure. The proposed algorithm integrates Principal Component Analysis (PCA) as a feature extraction technique with the minimum means of clusters and Euclidean Distance to achieve personal recognition. After indexing the training set for each person, the clustering of the differences is determined. The recognition of the person represented by the minimum mean index; this process returned with each reduction. The experimental results show that the recognition rate is 100% despite reducing the training sets to 44%, while the recognition rate decrease to 70% when the reduction reaches 89%. The clear picture out is the results of the proposed system support the idea of the redaction of training sets in addition to obtaining a high recognition rate based on application requirements.

scholarly journals Intelligent Badminton Training Robot in Athlete Injury Prevention Under Machine Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Jun Xie ◽  
Guohua Chen ◽  
Shuang Liu
Keyword(s):  
Machine Learning ◽  
Injury Prevention ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Training Data ◽  
Software Systems ◽  
Total Size ◽  
Data Set ◽  
The Moment ◽  
Athlete Injury

This study was developed to explore the role of the intelligent badminton training robot (IBTR) to prevent badminton player injuries based on the machine learning algorithm. An IBTR is designed from the perspectives of hardware and software systems, and the movements of the athletes are recognized and analyzed with the hidden Markov model (HMM) under the machine learning. After the design was completed, it was simulated with the computer to analyze its performance. The results show that after the HMM is optimized, the recognition accuracy or data pre-processing algorithm, based on the sliding window segmentation at the moment of hitting reaches 96.03%, and the recognition rate of the improved HMM to the robot can be 94.5%, showing a good recognition effect on the training set samples. In addition, the accuracy rate is basically stable when the total size of the training data is 120 sets, after the accuracy of the robot is analyzed through different data set sizes. Therefore, it was found that the designed IBTR has a high recognition rate and stable accuracy, which can provide experimental references for injury prevention in athlete training.

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

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