On the importance of domain adaptation in texture classification

Texture classification algorithms require generalization abilities in order to be reliably used in real world applications. This paper casts this problem in the domain adaptation setting and presents the first study investigating (a) up to which extent this visual recognition problem suffers from this issue, and (b) the effectiveness of existing domain adaptation algorithms in mitigating it. We focus on domain adaptation methods based on shallow classifiers, and test their performance on deep and non deep features. Results obtained on a newly created domain adaptation texture setup show the superiority of deep features compared to other well known approaches, and highlights the importance of factoring in the domain shift when dealing with textures in the wild.