A scene image classification technique for a ubiquitous visual surveillance system

Kaljahi, Maryam Asadzadeh and Shivakumara, Palaiahnakote and Anisi, Mohammad Hossein and Idris, Mohd Yamani Idna and Blumenstein, Michael and Khan, Muhammad Khurram (2019) A scene image classification technique for a ubiquitous visual surveillance system. Multimedia Tools and Applications, 78 (5). pp. 5791-5818. ISSN 1380-7501, DOI https://doi.org/10.1007/s11042-018-6151-x.

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Official URL: https://doi.org/10.1007/s11042-018-6151-x


The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resources based on ubiquitous monitoring systems. However, existing visual ubiquitous monitoring systems have only been developed for a specific purpose. As a result, they cannot be used for different scenarios. To overcome this challenge, this paper presents a new ubiquitous visual surveillance mechanism based on classification of scene images. The proposed mechanism supports different applications including Soil, Flood, Air, Plant growth and Garbage monitoring. To classify the scene images of the monitoring systems, we introduce a new technique, which combines edge strength and sharpness to detect focused edge components for Canny and Sobel edges of the input images. For each focused edge component, a patch that merges nearest neighbor components in Canny and Sobel edge images is defined. For each patch, the contribution of the pixels in a cluster given by k-means clustering on edge strength and sharpness is estimated in terms of the percentage of pixels. The same percentage values are considered as a feature vector for classification with the help of a Support Vector Machine (SVM) classifier. Experimental results show that the proposed technique outperforms the state-of-the-art scene categorization methods. Our experimental results demonstrate that the SVM classifier performs better than rule and template-based methods. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Item Type: Article
Funders: Faculty of Computer Science and Information Technology, University of Malaya under a special allocation of Post Graduate Funding for the RP036B-15AET project, Dean of Scientific Research at King Saud University for funding this work through Research Group Number (RGP-288)
Uncontrolled Keywords: Edge strength; Focused edges; Image classification; K-means clustering; Sharpness; SVM classifier; Ubiquitous visual surveillance
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Apr 2020 05:02
Last Modified: 08 Apr 2020 05:02
URI: http://eprints.um.edu.my/id/eprint/24166

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