scholarly journals Maize Leaf Disease Image Classification Using Bag of Features

2019 ◽  
Vol 11 (2) ◽  
pp. 48
Author(s):  
Mohammad Syarief ◽  
Novi Prastiti ◽  
Wahyudi Setiawan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Grid Method ◽  
Support Vector ◽  
Point Location ◽  
Maize Leaf ◽  
Bag Of Features ◽  
Speed Up ◽  
Extraction Stage ◽  
Speed Up Robust Feature

Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70%  and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%,  and 85%.

scholarly journals Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

2015 ◽  
Vol 72 ◽  
pp. 24-30 ◽  
Author(s):  
Ryfial Azhar ◽  
Desmin Tuwohingide ◽  
Dasrit Kamudi ◽  
Sarimuddin ◽  
Nanik Suciati
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Classification ◽  
Support Vector ◽  
Sift Feature ◽  
Bag Of Features

scholarly journals KLASIFIKASI KATEGORI CITRA DIGITAL DENGAN METODE BAG OF VISUAL WORDS

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 153-160
Author(s):  
Mahardika Abdi Prawira Tanjung
Keyword(s):  
Support Vector Machine ◽  
Digital Image ◽  
Digital Images ◽  
Support Vector ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Performance Quality ◽  
Speed Up ◽  
Speed Up Robust Feature ◽  
Human Eyes

Abstract: The human eye can distinguish objects from digital images, however, computers do not have the ability as human eyes that can directly distinguish objects from digital images. Therefore the bag of visual words method was created. Bag of visual words is a method for presenting digital images based on local features. Bag of visual words illustrates how an image can be taken its characteristics, so that computers can distinguish objects on digital images. The test results show that the bag of visual words are still not maximal in classifying digital image categories, especially the chair category, which is only able to produce the most accurate accuracy of 75%. To improve the performance quality of bag of visual words in classifying digital image categories, especially the chair category, you can add an approach to determine the good number of K in clustering the visual words pattern.            Keywords: Bag Of Visual Words, Classification, Digital Image, Speed-Up Robust Feature, Support Vector Machine   Abstrak: Secara kasat mata manusia bisa membedakan objek pada citra digital, namun, komputer tidak memiliki kemampuan sebagai mata manusia yang dapat secara langsung membedakan objek pada citra digital. Maka dari itu diciptakanlah metode bag of visual words. Bag of visual words adalah metode untuk menyajikan citra digital berdasarkan fitur lokal. Bag of visual words menggambarkan bagaimana suatu gambar dapat diambil karakteristiknya, sehingga komputer dapat membedakan objek pada citra digital. Hasil  pengujian  menunjukkan  bag of visual words   masih belum maksimal dalam  mengklasifikasi  kategori citra digital khususnya kategori chair, yang hanya mampu menghasilkan akurasi paling akurat sebesar 75 %. Untuk       meningkatkan        kualitas kinerja bag of visual words dalam mengklasifikasi kategori citra digital khususnya kategori chair, dapat menambahkan pendekatan untuk menentukan jumlah K yang baik dalam mengkluster pola visual words.  Kata kunci: Bag Of Visual Words, Klasifikasi, Citra Digital, Speed-Up Robust Feature, Support Vector Machine

SAR Image Classification by Support Vector Machine

2007 ◽  
pp. 341-353
Author(s):  
Toru Fujinaka ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Support Vector ◽  
Sar Image

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

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