Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks

Convolutional neural networks (CNNs) have greatly improved image classification performance. However, the extensive time required for classification owing to the large amount of computation involved, makes it unsuitable for application to low-performance devices. To speed up image classification, we propose a cached CNN, which can classify input images based on similarity with previously input images. Because the feature maps extracted from the CNN kernel represent the intensity of features, images with a similar intensity can be classified into the same class. In this study, we cache class labels and feature vectors extracted from feature maps for images classified by the CNN. Then, when a new image is input, its class label is output based on its similarity with the cached feature vectors. This process can be performed at each layer; hence, if the classification is successful, there is no need to perform the remaining convolution layer operations. This reduces the required classification time. We performed experiments to measure and evaluate the cache hit rate, precision, and classification time.