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Article

A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection

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Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
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College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia
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School of Architecture Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
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Department of EE, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
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College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(12), 5037; https://doi.org/10.3390/su12125037
Received: 18 April 2020 / Revised: 5 June 2020 / Accepted: 17 June 2020 / Published: 19 June 2020
(This article belongs to the Special Issue Research on Sustainability and Artificial Intelligence)
With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation. View Full-Text
Keywords: object classification; deep learning; features fusion; features selection; recognition object classification; deep learning; features fusion; features selection; recognition
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MDPI and ACS Style

Rashid, M.; Khan, M.A.; Alhaisoni, M.; Wang, S.-H.; Naqvi, S.R.; Rehman, A.; Saba, T. A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection. Sustainability 2020, 12, 5037. https://doi.org/10.3390/su12125037

AMA Style

Rashid M, Khan MA, Alhaisoni M, Wang S-H, Naqvi SR, Rehman A, Saba T. A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection. Sustainability. 2020; 12(12):5037. https://doi.org/10.3390/su12125037

Chicago/Turabian Style

Rashid, Muhammad, Muhammad A. Khan, Majed Alhaisoni, Shui-Hua Wang, Syed R. Naqvi, Amjad Rehman, and Tanzila Saba. 2020. "A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection" Sustainability 12, no. 12: 5037. https://doi.org/10.3390/su12125037

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