Semantic pixel sets based local binary patterns for face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Texture representations using subspace embeddings
Pattern Recognition Letters
Global matching to enhance the strength of local intensity order pattern feature descriptor
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
A new descriptor resistant to affine transformation and monotonic intensity change
Computer Vision and Image Understanding
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A novel local image descriptor is proposed in this paper, which combines intensity orders and gradient distributions in multiple support regions. The novelty lies in three aspects: 1) The gradient is calculated in a rotation invariant way in a given support region; 2) The rotation invariant gradients are adaptively pooled spatially based on intensity orders in order to encode spatial information; 3) Multiple support regions are used for constructing descriptor which further improves its discriminative ability. Therefore, the proposed descriptor encodes not only gradient information but also information about relative relationship of intensities as well as spatial information. In addition, it is truly rotation invariant in theory without the need of computing a dominant orientation which is a major error source of most existing methods, such as SIFT. Results on the standard Oxford dataset and 3D objects have shown a significant improvement over the state-of-the-art methods under various image transformations.