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

Nanne van Noord*, Eric Postma

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    125 Citations (Scopus)
    182 Downloads (Pure)


    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. (C) 2016 The Authors. Published by Elsevier Ltd.

    Original languageEnglish
    Pages (from-to)583-592
    Number of pages10
    JournalPattern Recognition
    Publication statusPublished - Jan 2017


    • Convolutional Neural Networks
    • Multi-scale
    • Artist Attribution
    • Scale-variant Features
    • VAN GOGH


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