Hybrid Featured based Pyramid Structured CNN for Texture Classification

Haoran Liu, Sei Ichiro Kamata, Yuqi Li

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ170-175
ページ数6
ISBN(電子版)9781728133775
DOI
出版ステータスPublished - 2019 9
イベント2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019 - Kuala Lumpur, Malaysia
継続期間: 2019 9 172019 9 19

出版物シリーズ

名前Proceedings 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

ASJC Scopus subject areas

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

フィンガープリント 「Hybrid Featured based Pyramid Structured CNN for Texture Classification」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル