LOAD: Local orientation adaptive descriptor for texture and material classification

• This paper proposes a novel and discriminative texture descriptor (Local Orientation Adaptive Descriptor).
• We build a new real-world material data set that contains 13 categories.
• We demonstrate the effectiveness of our feature on texture and real-world material classification.
• We experimentally demonstrate that the proposed LOAD shows strong complementary property with the learning based features, such as CNN.

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.