Hard Decorrelated Centralized Loss for Fine-Grained Image Retrieval

Abstract Although there is abundant of investigations on fine-grained image retrieval, it is still an extremely challenging task in the field of computer vision, due to the character of small diversity in inter-class but large diversity within intra-class. To handle this task, loss functions are critical to the performance of a deep convolutional neural network in extracting the discriminative feature of the fine-grained image for retrieval. Recent studies showed that the global structure loss functions help to extract more discriminative features. In this paper, we introduce a novel global structure loss function, named Hard Decorrelated Centralized Loss, for further improving the representation for fine-grained image retrieval. The proposed loss is available in extracting the discriminative feature for dividing the most similar categories. In our experiments, we employ the proposed loss to train the convolutional neural network, which shows state-of-the-art performances on six classical fine-grained image retrieval benchmarks, e.g. CUB-200-2011 and Stanford Cars.