Fine-grained visual categorization of butterfly specimens at sub-species level via a convolutional neural network with skip-connections

Abstract

Fine-grained visual categorization often suffers from the challenges that the subordinate categories within an entry-level category can only be distinguished by subtler discriminations. It demands an effective algorithm to learn a multiple-perspective density distribution for precious categorization. To shape a coarse-to-fine object perception, a hierarchical convolutional neural network (CNN) denoting the skip-connections convolutional neural network (S-CCNN) was proposed, focusing on a butterfly domain at sub-species level due to the fine-grained structure of the category taxonomy. Specifically, based on the serial backbone, three skip-connections with Grating layers are established to link the earlier layers and upper layers of network, and integrated with DropConnect, exponential linear unit (ELU), and local response normalization (LRN) to alleviate over-fitting and vanishing gradient. Benefitting from the long-span skip-connections, coarse-grained context with orientation descriptions and finer-grained context with semantic representations can be both took into consideration, and they are jointly incorporated into framework. S-CCNN can achieve the cross-utilization of object-level and part-level representations, and its rationality were evidenced with both theory and practice. For effectness verification, a total of 24,836 lab-made images of butterfly specimens spanning 56 sub-species are utilized as testing samples, while 173,852 augmented images are employed for model training. S-CCNN delivers a consistent and significant boost in performance, i.e., validation accuracy achieved 94.17% and testing accuracy achieved 93.36%, which outperformed state-of-the-arts. S-CCNN can easily relish accuracy gains from skip-connections in fine-grained visual categorization of butterfly sub-species, without any bells and whistles.