Hybrid Featured based Pyramid Structured CNN for Texture Classification

Haoran Liu, Sei Ichiro Kamata, Yuqi Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Texture is always considered as the preconscious for human vision. Texture also remains the same significance in computer vision field that can be used to help in detection, segmentation and classification tasks. Since texture is a global feature inherent in an image, containing essential surface information, which can be described in detail and hardly affected by image noises. We propose a novel end-to-end structure to make use of hybrid features by a mixture network and improve the classification accuracy, mainly combining Gray Level Co-occurrence Matrix (GLCM) statistical features together with pyramid structured deep convolutional neural networks (Pyramid CNNs) features in a paralleling network structure. Considering GLCM is a remarkable descriptor for texture statistical features, it can compensate the missing information in the convolution and pooling process of CNN and decline overfitting problems. Meanwhile, multi-resolution image pyramid structured CNN helps to capture both global features and local features. Quantitively, we carry out experiments on widely used datasets and results show that the GLCM and Pyramid CNN features merged structure obtains maximum 6.8% improvement comparing to the basic CNN methods.

Original languageEnglish
Title of host publicationProceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-175
Number of pages6
ISBN (Electronic)9781728133775
DOIs
Publication statusPublished - 2019 Sep
Event2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019 - Kuala Lumpur, Malaysia
Duration: 2019 Sep 172019 Sep 19

Publication series

NameProceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019

Conference

Conference2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019
CountryMalaysia
CityKuala Lumpur
Period19/9/1719/9/19

Keywords

  • convolutional neural network
  • eo-occurrence matrix
  • feature extraction
  • texture classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics
  • Artificial Intelligence

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