Searching by parts: Towards fine-grained image retrieval respecting species correlation

Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlations such as the same ancestors or similar habits, which are helpful in taxonomy and understanding biological traits. In this paper, we devise a new fine-grained retrieval task that searches for similar instances from different species based on body parts. To this end, we propose a two-step strategy. In the first step, we search for visually similar parts to a query image using a deep convolutional neural network (CNN). To improve the quality of the retrieved candidates, structural cues are introduced into the CNN using a novel part-pooling layer, in which the receptive field of each part is adjusted automatically. In the second step, we re-rank the retrieved candidates to improve the species diversity. We achieve this by formulating a novel ranking function that balances between the similarity of the candidates to the queried parts, while decreasing the similarity to the query species. We provide experiments on the benchmark CUB200 dataset and Columbia Dogs dataset, and demonstrate clear benefits of our schemes.