Fine-grained visual categorization with fine-tuned segmentation
Fine-grained visual categorization (FGVC) refers to the task of classifying objects that belong to the same basic-level class (e.g., different bird species). Since the subtle inter-class variation often exists on small parts (e.g., beak, belly, etc.), it is reasonable to localize semantic parts of an object before describing it. However, unsupervised part-segmentation methods often suffer from over-segmentation which harms the quality of image representation. In this paper, we present a fine-tuning approach to tackle this problem. To this end, we perform a greedy algorithm to optimize an intuitive objective function, preserving principal parts meanwhile filtering noises, and further construct mid-level parts beyond the refined parts toward a more descriptive representation. Experiments demonstrate that our approach achieves competitive classification accuracy on the CUB-200-2011 dataset with both Fisher vectors and deep conv-net features.