Abstract

Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic class-es. One major challenge in FGVC is the co-occurrence of two issues: 1) many subordinate classes are highly correlated and are difficult to distinguish, and 2) there exists thelarge intra-class variation (e.g., due to object pose). This paper proposes to explicitly address the above two issues vi-a distance metric learning (DML). DML addresses the first issue by learning an embedding so that data points from the same class will be pulled together while those from different classes should be pushed apart from each other; and it addresses the second issue by allowing the flexibility that only a portion of the neighbors (not all data points) from the same class need to be pulled together. However, feature representation of an image is often high dimensional, and DML is known to have difficulty in dealing with high dimensional feature vectors since it would require O(d2 ) for storage and O(d3 ) for optimization. To this end, we proposed a multi-stage metric learning framework that dividesthe large-scale high dimensional learning problem to a se-ries of simple subproblems, achieving O(d) computational complexity. The empirical study with FVGC benchmark datasets verifies that our method is both effective and efficient compared to the state-of-the-art FGVC approaches.