Exploiting Coarse-To-Fine Mechanism For Fine-Grained Recognition

Fine-grained object recognition is more challenging than generic categorization due to the subtle difference between subcategories under the large intra-class pose change and appearance variations. The state-of-the-art fine-grained recognition methods usually utilize part detection or pose alignment to alleviate the pose variation, and then use convolutional neural networks (CNNs) to extract local discriminative features. Although the hierarchical structure of deep CNNs enables rich and discriminative visual feature extraction, the recognition methods so far mostly use the features of only the last convolutional layer for classification. In this paper, by exploiting the correlation of the convolutional features of within-layer and between-layer, we propose a method to integrate multi-layer convolutional features based on coarse-to-fine mechanism for improving the discrimination capability. Experiments on a number of public datasets show that the proposed method, without part annotation or pose alignment, yields superior or comparable performance to the state-of-the-art methods.