ImageNet Classification using ResNet50 and Binary Marine Predators Algorithm
Noor Muhammed Noori, Omar Saber Qasim · 2023
Image classification accuracy relies heavily on the quality of features used to classify the dataset. Researchers commonly use metaheuristic algorithms like the Marine Predators Algorithm (MPA) for feature selection and dimensionality reduction to enhance the accuracy and reduce the number of features required. In this study, we used a ResNet-50 convolutional neural network (CNN) pre-trained on ImageNet to extract features from the image dataset. We then utilized the Binary Marine Predator Algorithm (BMPA) to filter out irrelevant features and select the most pertinent ones from the feature vector extracted from the “avg_pool” layer in ResNet-50. Our proposed efficient approach can achieve superior classification accuracy and optimal feature selection. We anticipate that the combination of CNN and BMPA can be utilized in various other image classification tasks to enhance their performance.