Binary Image Classification using Parallel Neural Networks

Abstract

Image Recognition and Classification techniques drives the applied sciences of Image Processing and Computer Vision. Major challenges of image recognition and classification includes time-consumption, inefficient results, learning delay and poor accuracy in classification of images. Recent advancements in cutting-edge technologies address these issues to a greater extent in view of the software, hardware and technology used. Internet of Things (IoT) increases the demand of image classification using enhanced capabilities of Convolution Neural Networks (CNN). Deep Neural Learning capabilities enhances the accuracy rates due to optimized and shared parameters, reduces time consumed in processing due to the application of multiple filters by using pooling layers. Pooling layers outperform the defects of normalization layers. Stochastic Gradient Descent (SGD) optimizer optimizes the objective function by identifying proper smoothing parameters at low convergence rate. The current work performed using Transfer Learning (TL) using Mobilenet, sequential model using SGD optimizer over ReLU and softmax activation functions. Experimental results prove that the proposed method achieves promising classification accuracy for large data of images.