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

Muhammad Rashid, Muhammad Attique Khan, Majed Alhaisoni, Shui-Hua Wang, Syed Rameez Naqvi, Amjad Rehman and Tanzila Saba
Additional contact information
Muhammad Rashid: Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
Muhammad Attique Khan: Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
Majed Alhaisoni: College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia
Shui-Hua Wang: School of Architecture Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Syed Rameez Naqvi: Department of EE, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan
Amjad Rehman: College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Tanzila Saba: College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

Sustainability, 2020, vol. 12, issue 12, 1-21

Abstract: 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.

Keywords: object classification; deep learning; features fusion; features selection; recognition (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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