Image Classification and Detection of Insulators using Bag of Visual Words and Speeded up Robust Features
Ayushi Jadia1, M.P.S. Chawla2
1Ayushi Jadia*, Department of Electrical Engineering, SGSITS, Indore, India.
2M.P.S. Chawla, Department of Electrical Engineering, SGSITS, Indore, (M.P.), India.
Manuscript received on September 05, 2020. | Revised Manuscript received on September 12, 2020. | Manuscript published on September 15, 2020. | PP: 7-13 | Volume-6, Issue-10, September 2020. | Retrieval Number: 100.1/ijisme.J12600961020 | DOI: 10.35940/ijisme.J1260.0961020
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Electrical substation online monitoring in computer vision technology is based on image processing algorithm to perform visual analysis. This paper presents classification of ceramic and glass insulators through Bag of Visual Words and detection of these insulators by Point Feature Matching. The training image datasets are used for categorization by forming a visual vocabulary while a new unlabeled image from testing image dataset is classify using nearest neighbor classification method for features descriptor. For detection we use Speeded up Robust Features for detecting position of insulator present in cluttered scene image. Matching process is done between test and reference image and decision is made based on similar features. We conducted experiment on insulators to verify the superiority of our proposed method. The proposed method can be used in security, surveillance and inspection system.
Keywords: Classification, Bag of Visual Words (BOVW), K-Nearest Neighbor, Detection, Point Feature matching, Speeded up Robust Features (SURF), Insulators.