Hybrid Mayfly Lévy Flight Distribution Optimization Algorithm-Tuned Deep Convolutional Neural Network for Indoor–Outdoor Image Classification

Abstract

Image classification in the image is the persistent task to be computed in robotics, automobiles, and machine vision for sustainability. Scene categorization remains one of the challenging parts of various multi-media technologies implied in human–computer communication, robotic navigation, video surveillance, medical diagnosing, tourist guidance, and drone targeting. In this research, a Hybrid Mayfly Lévy flight distribution (MLFD) optimization algorithm-tuned deep convolutional neural network is proposed to effectively classify the image. The feature extraction process is a significant task to be executed as it enhances the classifier performance by reducing the execution time and the computational complexity. Further, the classifier is optimally trained by the Hybrid MLFD algorithm which in turn reduces optimization issues. The accuracy of the proposed MLFD-based Deep-CNN using the SCID-2 dataset is 95.2683% at 80% of training and 97.6425% for 10 K-fold. This manifests that the proposed MLFD-based Deep-CNN outperforms all the conventional methods in terms of accuracy, sensitivity, and specificity.