  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 proposed LOAD yields the state-of-the-art performance. It is worth to
mention that we achieve a 65.4\% classification accuracy-- which is, to the
best of our knowledge, the highest record by far --on Flickr Material Database
by using a single feature. Moreover, by combining LOAD with the feature
extracted by 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.
