BoFF: A bag of fuzzy deep features for texture recognition

Abstract

Here, we propose a novel method for texture recognition that employs fuzzy modeling over deep learning features. Specifically, the well-established pipeline of deep filter banks for texture description is followed, but using fuzzy equivalence measures for aggregating the deep features. This solution is more robust than a simple “all-or-nothing” assignment used on bag-of-visual-words, and it is less expensive than complex statistical representations such as Fisher vectors. Additionally, it avoids dependence on strong assumptions about specific distributions. The proposed method is evaluated on texture classification tasks, including both benchmark databases and a practical task in botany. In both cases, the results were competitive with state-of-the-art methods and suggest the potential of this combination for texture analysis in general.