Learning scale-variant and scale-invariant features for deep image classification

• DOI: 10.1016/j.patcog.2016.06.005
• Publication Date: 2016-06-11T23:13:42Z
• Authors: Nanne van Noord; Eric Postma
• Keywords: FOS: Computer and information sciences; Scale-variant Features; Computer Vision and Pattern Recognition (cs.CV); VAN GOGH; Convolutional Neural Networks; Computer Science - Computer Vision and Pattern Recognition; Artist Attribution; 02 engineering and technology; Artificial Intelligence; Signal Processing; 0202 electrical engineering, electronic engineering, information engineering; Computer Vision and Pattern Recognition; Multi-scale; Software

Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scale-invariant and scale-variant representations. We propose a multi- scale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance.