scholarly journals Classification of Road Signs Using Color, Shape and Texture Feature Coefficients by SOM

2007 ◽  
Vol 19 (5) ◽  
pp. 466-475
Author(s):  
Hirohumi OHARA ◽  
Akihiro KANAGAWA
Keyword(s):  
Texture Feature ◽  
Road Signs

scholarly journals The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Christophe Chesneau ◽  
Muhammad H. Tahir ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medicinal Plant ◽  
Texture Feature ◽  
Stevia Rebaudiana ◽  
Ocimum Sanctum ◽  
Multi Layer Perceptron ◽  
Plant Leaves ◽  
Chi Square ◽  
Lemon Balm

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

scholarly journals Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziting Zhao ◽  
Tong Liu ◽  
Xudong Zhao
Keyword(s):  
Variable Selection ◽  
Gabor Filter ◽  
Texture Feature ◽  
Concrete Surface ◽  
Automatic Classification ◽  
Ensemble Classification ◽  
Image Texture ◽  
Inspection Time ◽  
Selection Strategy

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

Using Texture Feature in Fruit Classification

2021 ◽  
Vol 39 (1B) ◽  
pp. 67-79
Author(s):  
Mauj H. Abd al kreem ◽  
Abd allameer A. Karim
Keyword(s):  
Fruits And Vegetables ◽  
Real Life ◽  
Texture Feature ◽  
Extraction Methods ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Tree Classifier ◽  
Fresh Products ◽  
Occurrence Matrix

Recent advances in computer vision have allowed wide-ranging applications in every area of ​​life. One such area of ​​application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the  extracted  features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets  is  used  in this work,   where 70% of the dataset were used  in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree  classifier have been used to  classifying  fruits 360 images, results show that the the three feature extraction methods  give a promising results , while the HoG method yielded a poerfull results in which  the accuracy obtained is 96%.

Texture feature coding method for classification of liver sonography

2002 ◽  
Vol 26 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Ming-Huwi Horng ◽  
Yung-Nien Sun ◽  
Xi-Zhang Lin
Keyword(s):  
Texture Feature ◽  
Coding Method
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