  Discriminative localization is essential for fine-grained image
classification task, which devotes to recognizing hundreds of subcategories in
the same basic-level category. Reflecting on discriminative regions of objects,
key differences among different subcategories are subtle and local. Existing
methods generally adopt a two-stage learning framework: The first stage is to
localize the discriminative regions of objects, and the second is to encode the
discriminative features for training classifiers. However, these methods
generally have two limitations: (1) Separation of the two-stage learning is
time-consuming. (2) Dependence on object and parts annotations for
discriminative localization learning leads to heavily labor-consuming labeling.
It is highly challenging to address these two important limitations
simultaneously. Existing methods only focus on one of them. Therefore, this
paper proposes the discriminative localization approach via saliency-guided
Faster R-CNN to address the above two limitations at the same time, and our
main novelties and advantages are: (1) End-to-end network based on Faster R-CNN
is designed to simultaneously localize discriminative regions and encode
discriminative features, which accelerates classification speed. (2)
Saliency-guided localization learning is proposed to localize the
discriminative region automatically, avoiding labor-consuming labeling. Both
are jointly employed to simultaneously accelerate classification speed and
eliminate dependence on object and parts annotations. Comparing with the
state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach
achieves both the best classification accuracy and efficiency.

Computer Vision and Pattern Recognition