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Learning rotation invariant convolutional filters for texture classification


Marcos, Diego; Volpi, Michele; Tuia, Devis (2016). Learning rotation invariant convolutional filters for texture classification. In: 23rd International Conference on Pattern Recognition, Cancún (México), 4 December 2016 - 8 December 2016. arXiv, 1-6.

Abstract

We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.

Abstract

We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN). We encode rotation invariance directly in the model by tying the weights of groups of filters to several rotated versions of the canonical filter in the group. These filters can be used to extract rotation invariant features well-suited for image classification. We test this learning procedure on a texture classification benchmark, where the orientations of the training images differ from those of the test images. We obtain results comparable to the state-of-the-art. Compared to standard shallow CNNs, the proposed method obtains higher classification performance while reducing by an order of magnitude the number of parameters to be learned.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Computer Vision and Pattern Recognition
Language:English
Event End Date:8 December 2016
Deposited On:14 Feb 2017 09:18
Last Modified:26 Jan 2022 12:27
Publisher:arXiv
OA Status:Closed
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/ICPR.2016.7899932
Official URL:https://arxiv.org/pdf/1604.06720v2
Related URLs:https://iapr.papercept.net/conferences/conferences/ICPR16/program/ICPR16_ContentListWeb_3.html (Organisation)