Polynomial self-similarity for object classification

Objects in an image may be semantically similar not because they share common photometric properties, but because they share common recurring patterns of internal self-similarities. In this paper, a polynomial self-similarity approach for object classification is proposed. Extending the global self-similarity framework, polynomial self-similarity enables greater flexibility in matching details with similar structure but intensity differences, and details under different ambient illumination. Experiments show that the proposed approach provides classification accuracy that is competitive with standard global self-similarity, even under challenging non-uniform illumination conditions.