Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization

Highlights：

• To the best of our knowledge, Mask-CNN is the first end-to-end model that selects deep convolutional descriptors for object recognition, especially for fine-grained image recognition.

• We present a novel and efficient part-based three-stream model for fine-grained recognition. By discarding the fully connected layers, the proposed M-CNN is computationally efficient (cf. Table 1 and Table 4 in experiments). Additionally, comparing with state-of-the-art methods, M-CNN has smaller feature dimensionality. Beyond those, it achieves the highest classification accuracy on CUB200-2011 and Birdsnap among published methods.

• The part localization performance of the proposed model outperforms other part-based finegrained approaches which requires additional bounding boxes. In particular, M-CNN is 12.76% higher than state-of-the-art for head localization on CUB200-2011.