scholarly journals Classification of color features in butterflies using the Support Vector Machine (SVM)

2021 ◽  
Vol 2 (02) ◽  
pp. 83-87
Author(s):  
Dhian Satria Yudha Kartika ◽  
Hendra Maulana
Keyword(s):  
Support Vector Machine ◽  
Pieris Rapae ◽  
Color Space ◽  
Danaus Plexippus ◽  
Research Process ◽  
Extraction Process ◽  
Junonia Coenia ◽  
Support Vector ◽  
Color Quantization ◽  
Color Features

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2% Keywords—image processing; classification; butterflies; color features; features extraction

scholarly journals Predicting sensory evaluation of spinach freshness using machine learning model and digital images

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248769
Author(s):  
Kento Koyama ◽  
Marin Tanaka ◽  
Byeong-Hyo Cho ◽  
Yusaku Yoshikawa ◽  
Shige Koseki
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sensory Evaluation ◽  
Color Space ◽  
Local Features ◽  
Support Vector ◽  
Nondestructive Technique ◽  
Color Features ◽  
Artificial Neural ◽  
Four Levels

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.

Kernel Optimized-Support Vector Machine and MapReduce Framework for Sentiment Classification of Train Reviews

2019 ◽  
Vol 27 (06) ◽  
pp. 1025-1050
Author(s):  
Rashmi K. Thakur ◽  
Manojkumar V. Deshpande
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Mapreduce Framework ◽  
Mapreduce Model

Sentiment analysis is one of the popular techniques gaining attention in recent times. Nowadays, people gain information on reviews of users regarding public transportation, movies, hotel reservation, etc., by utilizing the resources available, as they meet their needs. Hence, sentiment classification is an essential process employed to determine the positive and negative responses. This paper presents an approach for sentiment classification of train reviews using MapReduce model with the proposed Kernel Optimized-Support Vector Machine (KO-SVM) classifier. The MapReduce framework handles big data using a mapper, which performs feature extraction and reducer that classifies the review based on KO-SVM classification. The feature extraction process utilizes features that are classification-specific and SentiWordNet-based. KO-SVM adopts SVM for the classification, where the exponential kernel is replaced by an optimized kernel, finding the weights using a novel optimizer, Self-adaptive Lion Algorithm (SLA). In a comparative analysis, the performance of KO-SVM classifier is compared with SentiWordNet, NB, NN, and LSVM, using the evaluation metrics, specificity, sensitivity, and accuracy, with train review and movie review database. The proposed KO-SVM classifier could attain maximum sensitivity of 93.46% and 91.249% specificity of 74.485% and 70.018%; and accuracy of 84.341% and 79.611% respectively, for train review and movie review databases.

scholarly journals Personality Analysis through Handwriting Detection Using Android Based Mobile Device

2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Waskitha Wijaya ◽  
Herman Tolle ◽  
Fitri Utaminingrum3
Keyword(s):  
Support Vector Machine ◽  
Personality Traits ◽  
Mobile Device ◽  
Extraction Process ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method

Graphology is one of the psychology disciplines which aims to study the personality traits of individuals through interpretation of handwriting. We can get information of one’s personality through graphology. In addition, by using android based mobile device, graphology analysis could show one’s personality faster. This study was conducted by taking 42 samples of handwriting from different backgrounds. The feature used in this study was handwriting margin. Besides, Support Vector Machine method was employed to classify the result feature from extraction process. The result of this study showed the accurate average of the application reached 82.738%.

scholarly journals Multi-color space learning for image segmentation based on a support vector machine

OSA Continuum ◽  
2019 ◽  
Vol 2 (11) ◽  
pp. 3050 ◽  
Author(s):  
Renzheng Zhang ◽  
Guodong Chen ◽  
Zheng Wang ◽  
Wenzheng Chi ◽  
Zhenhua Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Segmentation ◽  
Color Space ◽  
Support Vector

The Robot Target Recognition Based on Support Vector Machine and D-S Evidence Theory

2011 ◽  
Vol 308-310 ◽  
pp. 1215-1219
Author(s):  
Meng Si Zhu ◽  
Xiang Jun Zou ◽  
Li Juan Chen ◽  
Hai Xin Zou ◽  
Ke Yin Chen
Keyword(s):  
Support Vector Machine ◽  
Target Recognition ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Support Vector ◽  
Complex Environments ◽  
Color Features ◽  
Basic Probability ◽  
Geometry Feature ◽  
Feature Information

Aiming at the problem of low recognition rate and easy affected by environment during the process of robot target recognition in complex environments, the target recognition method combining support vector machine (SVM) with D-S evidence theory was proposed. Taking citrus recognition as an example, SVM was used by the method to local classification according to citrus color and geometry feature information respectively, and the results of SVM were transformed to probability outputs through Platt model, and treated them as the basic probability assignment (BPA) of D-S evidence theory to reason and fuse local recognition results, and then realized the combination of SVM and D-S evidence theory in citrus recognition, finally improved the recognition rate. The experimental results showed that: the recognition rate of the method combining SVM with D-S evidence theory and integrating color features and geometry features was higher than SVM method with only color or geometry features.

scholarly journals Klasifikasi Untuk Menentukan Tingkat Kematangan Buah Pisang Sunpride

2016 ◽  
Vol 2 ◽  
pp. 109
Author(s):  
Gracelia Adelaida Bere ◽  
Elizabeth Nurmiyati Tamtjita ◽  
Anggraini Kusumaningrum
Keyword(s):  
Euclidean Distance ◽  
Color Space ◽  
Extraction Process ◽  
Classification Performance ◽  
Test Image ◽  
K Nearest Neighbors ◽  
Color Features ◽  
Training Samples ◽  
Rgb Color Space ◽  
Test Objects

         YIQ (Iuma, In-phase, Quadrature) is a color space used to transmit analog TV signal. This research is conducting a possibility test on using YIQ as color features for fruit ripeness classification, which tested on Sunpride bananas. Classification is done using k-NN algorithm against YIQ values of several ripeness stage. The classification process itself consists of two steps: training and testing. In the training step, values from RGB color space of the images as training samples are converted into YIQ and extracted as features to form the classes, while in testing step, the test image went through the same conversion and feature extraction process, then classified using k-NN against the classes’ features, using k=3 and k=1. There are 120 Sunpride banana images used as test objects, and the results obtained shown that the classification performance using k=3 for Sangat Matang class is 100%, Busuk class is 66,67%, Mengkal class is 60% and Matang class is 60%. Results using k=1 for Sangat Matang class is 100%, Busuk class is 66,67%, Mengkal class is 66,7% and Matang class is 56,67%.Keywords  : Classification, YIQ Color Space, Banana Sunpride, Euclidean Distance, k-Nearest Neighbors

Sign in / Sign up

Export Citation Format

Share Document