Learning Gabor Texture Features for Fine-Grained Recognition
Lanyun Zhu Tianrun Chen Jianxiong Yin Simon See Jun Liu
Extracting and using class-discriminative features is critical for
fine-grained recognition. Existing works have demonstrated the possibility of
applying deep CNNs to exploit features that distinguish similar classes.
However, CNNs suffer from problems including frequency bias and loss of
detailed local information, which restricts the performance of recognizing
fine-grained categories. To address the challenge, we propose a novel texture
branch as complimentary to the CNN branch for feature extraction. We
innovatively utilize Gabor filters as a powerful extractor to exploit texture
features, motivated by the capability of Gabor filters in effectively capturing
multi-frequency features and detailed local information. We implement several
designs to enhance the effectiveness of Gabor filters, including imposing
constraints on parameter values and developing a learning method to determine
the optimal parameters. Moreover, we introduce a statistical feature extractor
to utilize informative statistical information from the signals captured by
Gabor filters, and a gate selection mechanism to enable efficient computation
by only considering qualified regions as input for texture extraction. Through
the integration of features from the Gabor-filter-based texture branch and
CNN-based semantic branch, we achieve comprehensive information extraction. We
demonstrate the efficacy of our method on multiple datasets, including
CUB-200-2011, NA-bird, Stanford Dogs, and GTOS-mobile. State-of-the-art
performance is achieved using our approach.