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

scholarly journals KLASIFIKASI EKSPRESI WAJAH MENGGUNAKAN BAG OF VISUAL WORDS

2018 ◽  
Vol 1 (2) ◽  
pp. 73 ◽  
Author(s):  
Muhathir Muhathir
Keyword(s):  
Visual Word ◽  
Interest Point ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Speed Up ◽  
Speed Up Robust Feature

<div><p class="Abstract">Pada hakikatnya, manusia dapat membedakan pola terhadap suatu objek berdasarkan bentuk visual yang mengandung keadaan emosional. Seperti membedakan ekspresi wajah seseorang pada suatu citra. Manusia dapat membedakan ekspresi pada citra tersebut secara kasat mata. Namun komputer yang tidak dapat mengenali ekspresi wajah tersebut. Bag of visual words merupakan suatu skema untuk mengklasifikasikan citra berdasarkan nilai-nilai pixel pada citra. Dengan menggunakan deteksi interest point dan ekstraksi interest point, bag of visual words mengambil ciri unik pada citra sehingga dapat membedakan pola-pola yang terdapat pada suatu citra. Bag of visual word dengan nilai K 500 mampu mengklasifikasi pola ekspresi wajah dengan tingkat akurasi 69%,</p></div>Kata kunci<strong>: </strong><em>Wajah, Klasifikasi, Speed-up Robust Feature, Bag of visual words, Ekspresi</em>

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 KLASIFIKASI EKSPRESI WAJAH MENGGUNAKAN BAG OF VISUAL WORDS

2018 ◽  
Vol 2 (2) ◽  
pp. 118-123
Author(s):  
Muhathir .
Keyword(s):  
Visual Word ◽  
Interest Point ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Speed Up ◽  
Speed Up Robust Feature

Pada hakikatnya, manusia dapat membedakan pola terhadap suatu objek berdasarkan bentuk visual yang mengandung keadaan emosional. Seperti membedakan ekspresi wajah seseorang pada suatu citra. Manusia dapat membedakan ekspresi pada citra tersebut secara kasat mata. Namun komputer yang tidak dapat mengenali ekspresi wajah tersebut. Bag of visual words merupakan suatu skema untuk mengklasifikasikan citra berdasarkan nilai-nilai pixel pada citra. Dengan menggunakan deteksi interest point dan ekstraksi interest point, bag of visual words mengambil ciri unik pada citra sehingga dapat membedakan pola-pola yang terdapat pada suatu citra. Bag of visual word dengan nilai K 500 mampu mengklasifikasi pola ekspresi wajah dengan tingkat akurasi 69%,Kata kunci: Wajah, Klasifikasi, Speed-up Robust Feature, Bag of visual words, Ekspresi

scholarly journals SURF Points Versus SIFT Points in Identification of Medicinal Plants

2019 ◽  
Vol 9 (2) ◽  
pp. 602-607
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Digital Image ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

Grey-based multiple instance learning with multiple bag-representative

2020 ◽  
Vol 33 (2) ◽  
pp. 59-73
Author(s):  
Lingyu Ren ◽  
Youlong Yang ◽  
Liqin Sun ◽  
Xu Wu
Keyword(s):  
Support Vector Machine ◽  
Multiple Instance Learning ◽  
Support Vector ◽  
Standard Assumption ◽  
Level Information ◽  
Speed Up ◽  
Instance Space ◽  
Grey Relational ◽  
Multiple Class

Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design efficient instance selection techniques to speed up the training process, without compromising the performance. Firstly, a multiple instance learning model of support vector machine based on grey relational analysis is proposed in this paper. The data size can be reduced, and the importance of instances in the bag can be preliminarily judged. Secondly, this paper introduces an algorithm with the bag-representative selector that trains the support vector machine based on bag-level information. Finally, this paper shows how to generalize the algorithm for binary multiple instance learning to multiple class tasks. The experimental study evaluates and compares the performance of our method against 8 state-of-the-art multiple instance methods over 10 datasets, and then demonstrates that the proposed approach is competitive with the state-of-art multiple instance learning methods.

Weld Defect Classification in Radiographic Film Using Statistical Texture and Support Vector Machine

2014 ◽  
Vol 896 ◽  
pp. 695-700
Author(s):  
Muhtadan ◽  
Risanuri Hidayat ◽  
Widyawan ◽  
Fahmi Amhar
Keyword(s):  
Support Vector Machine ◽  
Digital Image ◽  
Median Filter ◽  
Image Processing Technique ◽  
Support Vector ◽  
Radiographic Film ◽  
Machine Method ◽  
Defect Type ◽  
Weld Defects ◽  
Weld Defect

Weld defect identification requires radiographic operator experience, so the interpretation of weld defect type could potentially bring subjectivity and human error factor. This paper proposes Statistical Texture and Support Vector Machine method for weld defect type classification in radiographic film. Digital image processing technique applied in this paper implements noise reduction using median filter, contrast stretching, and image sharpening using Laplacian filter. Statistical method feature extraction based on image histogram was proposed for describing weld defects texture characteristic of a radiographic film digital image. Multiclass Support Vector Machine (SVM) algorithm was used to perform classification of weld defects type. The result of classification testing shows that the proposed method can classify 83.3% correctly from 60 testing data of weld defects radiographic films.

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