Affiliations: [a] College of Computer Science and Technology, Harbin Engineering University, Harbin, China
| [b] Wuhan Digital Engineering Research Institute, Wuhan, China
| [c] College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
Abstract: Fine-Grained ship classification is quite challenging because the visual differences between the subcategories are small. Due to the large intra-class similarity, it is very difficult to classify the ship objects without bounding box/part annotations. In this paper, we propose a model that combines multiple deep CNN features and use fusion strategies to explore of multi-scale features relationship. Because different levels/depths CNN features have different properties, so we combine multiple low-level local CNN features with high-level global CNN feature for object classification. The model shows a good way of tailoring pre-trained CNN models to fine-grained ship classification, which have lower cost in computation and storage compared with some state-of-the-art CNN methods and achieves the significant classification performances in FGVC-Aircraft and Stanford Cars datasets.
