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.

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.

TWO-STAGE METRIC LEARNING PROCEDURE FOR FACIAL SIGNATURE AUTHENTICATION

2007 ◽  
Vol 04 (02) ◽  
pp. 91-105
Author(s):  
TAKAMI SATONAKA ◽  
KEIICHI UCHIMURA
Keyword(s):  
Metric Learning ◽  
Recognition Rate ◽  
Image Synthesis ◽  
Training Data ◽  
Support Vector ◽  
Synthesis Methods ◽  
Decision Boundary ◽  
Two Stage ◽  
Learning Procedure ◽  
Threshold Setting

We present the two-stage adaptive metric learning procedure with improved generalization of missing training data for facial signature authentication. The conventional learning models suffer from degraded recognition rates due to poor estimation of decision boundary to classify impostor patterns. The two-stage networks combine multiple image synthesis methods to assume mixture patterns of training classes and facilitate threshold setting of false acceptance/rejection rates. The margin size of a decision boundary is adjusted to input patterns obtained from synthesized images and depends on the choice of the mixing factor. The present method effectively reduces the margin of a class with an improved recognition rate to classify impostor patterns from 82.2% to 98.8%. Furthermore, we examine the margin structure of the mixture distributions by using the Support Vector Machines.

Effects of Class Imbalance Using Machine Learning Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 1-17
Author(s):  
Swati V. Narwane ◽  
Sudhir D. Sawarkar
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Model Accuracy ◽  
Data Set ◽  
Class Distribution ◽  
Imbalance Problem

Class imbalance is the major hurdle for machine learning-based systems. Data set is the backbone of machine learning and must be studied to handle the class imbalance. The purpose of this paper is to investigate the effect of class imbalance on the data sets. The proposed methodology determines the model accuracy for class distribution. To find possible solutions, the behaviour of an imbalanced data set was investigated. The study considers two case studies with data set divided balanced to unbalanced class distribution. Testing of the data set with trained and test data was carried out for standard machine learning algorithms. Model accuracy for class distribution was measured with the training data set. Further, the built model was tested with individual binary class. Results show that, for the improvement of the system performance, it is essential to work on class imbalance problems. The study concludes that the system produces biased results due to the majority class. In the future, the multiclass imbalance problem can be studied using advanced algorithms.

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%.

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 BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B7 ◽  
pp. 379-384 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Landscape Heterogeneity ◽  
Sample Selection ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Learning Stage ◽  
Vector Machines ◽  
Imbalanced Training Data

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

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 BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B7 ◽  
pp. 379-384
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan
Keyword(s):  
Support Vector Machines ◽  
Image Classification ◽  
Landscape Heterogeneity ◽  
Sample Selection ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Learning Stage ◽  
Vector Machines ◽  
Imbalanced Training Data

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

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