LOAD: Local orientation adaptive descriptor for texture and material classification

Xianbiao Qi, Guoying Zhao, Linlin Shen, Qingquan Li, Matti Pietikäinen

Research output: Journal PublicationArticlepeer-review

19 Citations (Scopus)


In this paper, we propose a novel local feature, called Local Orientation Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD, we proposed to define point description on an Adaptive Coordinate System (ACS), adopt a binary sequence descriptor to capture relationships between one point and its neighbors and use multi-scale strategy to enhance the discriminative power of the descriptor. The proposed LOAD enjoys not only discriminative power to capture the texture information, but also has strong robustness to illumination variation and image rotation. Extensive experiments on benchmark data sets of texture classification and real-world material recognition show that the LOAD yields the state-of-the-art performance. It is worth to mention that we achieve a superior classification accuracy on Flickr Material Database by using a single feature. Moreover, by combining LOAD with Convolutional Neural Networks (CNN), we obtain significantly better performance than both the LOAD and CNN. This result confirms that the LOAD is complementary to the learning-based features.

Original languageEnglish
Pages (from-to)28-35
Number of pages8
Publication statusPublished - 2016
Externally publishedYes


  • Convolutional neural network
  • Improved Fisher vector
  • Local orientation adaptive descriptor
  • Material recognition
  • Texture classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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