Assessing Image Analysis Filters as Augmented Input to Convolutional Neural Networks for Image Classification

Abstract

Convolutional Neural Networks (CNNs) have been proven very effective in image classification and object recognition tasks, often exceeding the performance of traditional image analysis techniques. However, training a CNN requires very extensive datasets, as well as very high computational burden. In this work, we test the hypothesis that if the input includes the responses of established image analysis filters that detect salient image structures, the CNN should be able to perform better than an identical CNN fed with the plain RGB images only. Thus, we employ a number of families of image analysis filter banks and use their responses to compile a small number of filtered responses for each original RGB image. We perform a large number of CNN training/testing repetitions for a 40-class building recognition problem, on a publicly available image database, using the original images, as well as the original images augmented by the compiled filter responses. Results show that the accuracy achieved by the CNN with the augmented input is consistently higher than that of the RGB image input, both in terms of different repetitions of the execution, as well as throughout the iterations of each repetition.