Image Classification Using Fusion of Multiple Neural Networks

Artificial neural network is widely used in applications including image classification. However, due to the limitation and drawbacks of a single neural network, the accuracy of the image recognition can be further improved by using multiple neural networks that have different focuses in the method. In this paper, a conventional neural network based on the shape mask of the object is first used to classify fashion images. Then a convolutional neural network (CNN) is applied to identify the same set of images. In order to take advantage of different neural network approaches, a weight method is proposed in this paper. By considering the recognition accuracy of each class for each approach, a fused decision is made. Experimental results show that the fusion of multiple neural networks provides better overall accuracy than the individual approach, and achieves a lower number of false classifications in five out of ten classes.