An Image Classification Algorithm Based on Multidomain Convolution Neural Network
Abstract
Deep Convolutional Neural Networks (CNNs) have outperformed humans in many computer vision tasks, such as object recognition and image classification, but it is almost impossible to run a large-scale CNN network structure in the platform and application scenarios with limited calculate ability. For the limited computing platform and application scenarios, we propose a novel CNN architecture: Multi-Domain CNN (MD-CNN). Multi-domain images are obtained by different pre-processing of the images input to the multi-domain convolutional neural network, and each image domain independently obtains the output features through a feedforward network. Then, the output features of multiple parallel multi-domain images are spliced together as the output characteristic of MD-CNN. This structure can effectively convert the network of CNN from depth to width, extract more efficient features quickly, which improves the training speed of the network. In the test of MNIST and CIFAR-10 datasets, the accuracy of our method reached 96.3% and 91.4% respectively, moreover, compared with the traditional CNN training speed increased about 1.5 times.
Keywords
Convolutional Neural Network, Deep learning, Image classification
DOI
10.12783/dtcse/wcne2017/19883
10.12783/dtcse/wcne2017/19883
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