Gabor Convolutional Networks

Abstract

Steerable properties dominate the design of traditional filters, e.g., Gabor filters, and endow features the capability of dealing with spatial transformations. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). In this paper, we propose a new deep model, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the orientation and scale changes. By only manipulating the basic element of DCNNs based on Gabor filters, i.e., the convolution operator, GCNs can be easily implemented and are compatible with any popular deep learning architecture. Experimental results demonstrate the super capability of our algorithm in recognizing objects, where the scale and rotation changes occur frequently. The proposed GCNs have much fewer learnable network parameters, and thus is easier to train with an endtoend pipeline. The source code will be here 1.

Publication Date

5-3-2018

Publication Title

Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018

Volume

2018-January

Number of Pages

1254-1262

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/WACV.2018.00142

Socpus ID

85048226286 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85048226286

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