Deep convolutional feature aggregation for fine-grained cultivar recognition

Abstract

Fine-grained cultivar recognition has recently attracted considerable attention from researchers in pattern recognition and botany. However, this problem is highly challenging because the differences between cultivated species are so subtle that it is difficult to distinguish them effectively. This article presents a novel deep convolutional feature aggregation approach for fine-grained cultivar recognition. First, we propose a description method of the regional convolution covariance feature (RCCF), which describes the subtle changes in cultivated species by accumulating low-level convolution features and has a strong discriminating ability. Second, we also improve the regional maximum activation of convolutions (RMAC) and present a multiresolution RMAC high-level convolutional feature. Finally, we combine complementary RCCF with multiresolution RMAC features for fine-grained cultivar recognition, which significantly improves the recognition accuracy of fine-grained cultivated plants. We have carried out extensive tests on four benchmark cultivar plant datasets. The results show that our approach achieves state-of-the-art recognition performance on four benchmark cultivar plant datasets, surpassing other plant species identification methods.