scholarly journals Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

2019 ◽  
Vol 280 ◽  
pp. 05023
Author(s):  
Muhammad Alkaff ◽  
Husnul Khatimi ◽  
Nur Lathifah ◽  
Yuslena Sari
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Scale Invariant Feature Transform ◽  
Primary Data ◽  
Support Vector ◽  
Scale Invariant ◽  
Svm Classification ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

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.

scholarly journals RESEARCH ON DIFFERENT FEATURE EXTRACTION AND MAMMOGRAM CLASSIFICATION TECHNIQUES

2019 ◽  
pp. 1-3
Author(s):  
Anita Kaklotar
Keyword(s):  
Feature Extraction ◽  
Association Rule ◽  
Support Vector ◽  
Common Disease ◽  
Extraction Techniques ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Mammogram Images

Breast cancer is the primary and the most common disease found among women. Today, mammography is the most powerful screening technique used for early detection of cancer which increases the chance of successful treatment. In order to correctly detect the mammogram images as being cancerous or malignant, there is a need of a classier. With this objective, an attempt is made to analyze different feature extraction techniques and classiers. In the proposed system we rst do the preprocessing of the mammogram images, where the unwanted noise and disturbances in the mammograms are removed. Features are then extracted from the mammogram images using Gray Level Co-Occurrences Matrix (GLCM) and Scale Invariant Feature Transform (SIFT). Finally, the features are classied using classiers like HiCARe (Classier based on High Condence Association Rule Agreements), Support Vector Machine (SVM), Naïve Bayes classier and K-NN Classier. Further we test the images and classify them as benign or malignant class.

scholarly journals Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Yohannes Yohannes ◽  
Siska Devella ◽  
William Hadisaputra
Keyword(s):  
Feature Extraction ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.

Application of Object Recognition With Shape-Index Identification and 2D Scale Invariant Feature Transform for Key-Point Detection

2018 ◽  
pp. 218-231
Author(s):  
Chiranji Lal Chowdhary
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Shape Index ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Local Descriptor ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Point Detection

Humans make object recognition look inconsequential. In this chapter, scale-invariant feature extraction and shape-index depiction are used on a range of images for identifying objects. The shape-index is attained and used as a local descriptor or key-point descriptor. First surface properties for shape index identification and second as 2D scale invariant feature transformed for key-point detection and feature extraction. The object recognition classification is compared results with shape-index identification and 2D scale-invariant feature transform for key-point detection with SIFT and SURF. The authors are using images from the ImageNet dataset, and with use of shift-index + SIFT descriptors, they are finding better accuracy at the classification stage.

scholarly journals Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 56 ◽  
Author(s):  
Soheila Gheisari ◽  
Daniel Catchpoole ◽  
Amanda Charlton ◽  
Zsombor Melegh ◽  
Elise Gradhand ◽  
...  
Keyword(s):  
Local Binary Pattern ◽  
Scale Invariant Feature Transform ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Encoding ◽  
Histological Images ◽  
Feature Transform ◽  
Scale Invariant Feature

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.

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