Supervised Image Classification of Multi-Spectral Images Based on Statistical Machine Learning

2006 ◽  
pp. 362-389
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Statistical Machine Learning ◽  
Supervised Image Classification

scholarly journals Object-Based Image Classification of Summer Crops with Machine Learning Methods

2014 ◽  
Vol 6 (6) ◽  
pp. 5019-5041 ◽  
Author(s):  
José Peña ◽  
Pedro Gutiérrez ◽  
César Hervás-Martínez ◽  
Johan Six ◽  
Richard Plant ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Object Based

scholarly journals Image Classification using CNN and Machine Learning

2019 ◽  
pp. 575-580
Author(s):  
G. Keerthi Devipriya ◽  
E. Chandana ◽  
B. Prathyusha ◽  
T. Seshu Chakravarthy
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Training Models ◽  
Classification Of Images ◽  
Respective Category

Here by in this paper we are interested for classification of Images and Recognition. We expose the performance of training models by using a classifier algorithm and an API that contains set of images where we need to compare the uploaded image with the set of images available in the data set that we have taken. After identifying its respective category the image need to be placed in it. In order to classify images we are using a machine learning algorithm that comparing and placing the images.

scholarly journals Studi Komparasi Metode Machine Learning untuk Klasifikasi Citra Huruf Vokal Hiragana

2021 ◽  
Vol 5 (3) ◽  
pp. 905
Author(s):  
Muhammad Afrizal Amrustian ◽  
Vika Febri Muliati ◽  
Elsa Elvira Awal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Image Classification ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Comparative Study

Japanese is one of the most difficult languages to understand and read. Japanese writing that does not use the alphabet is the reason for the difficulty of the Japanese language to read. There are three types of Japanese, namely kanji, katakana, and hiragana. Hiragana letters are the most commonly used type of writing. In addition, hiragana has a cursive nature, so each person's writing will be different. Machine learning methods can be used to read Japanese letters by recognizing the image of the letters. The Japanese letters that are used in this study are hiragana vowels. This study focuses on conducting a comparative study of machine learning methods for the image classification of Japanese letters. The machine learning methods that were successfully compared are Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbor. The results of the comparative study show that the K-Nearest Neighbor method is the best method for image classification of hiragana vowels. K-Nearest Neighbor gets an accuracy of 89.4% with a low error rate.

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

2020 ◽  
Vol 12 (1) ◽  
pp. 36-48
Author(s):  
Vijayaprabakaran K. ◽  
Sathiyamurthy K. ◽  
Ponniamma M.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Elderly People ◽  
Activity Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Vision

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

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