Learning deep and sparse feature representation for fine-grained object recognition

In this paper, we address fine-grained classification which is quite challenging due to high intra-class variations and subtle inter-class variations. Most modern approaches to fine-grained recognition are established based on convolutional neural networks (CNN). Despite the effectiveness, these approaches still suffer from two major problems. First, they highly rely on large sets of training data, but manually annotating numerous training data is expensive. Second, the learned feature presentations by these approaches are often of high dimensions, leading to less efficiency. To tackle the two problems, we present an approach where on-line dictionary learning is integrated into CNN. The dictionaries can be incrementally learned by leveraging a vast amount of weakly labeled data on the Internet. With these dictionaries, all the training and testing data can be sparsely represented. Our approach is evaluated and compared with the state-of-the-art approaches on the benchmark dataset, CUB-200-2011. The promising results demonstrate its superiority in both efficiency and accuracy.