  In this paper, we categorize fine-grained images without using any object /
part annotation neither in the training nor in the testing stage, a step
towards making it suitable for deployments. Fine-grained image categorization
aims to classify objects with subtle distinctions. Most existing works heavily
rely on object / part detectors to build the correspondence between object
parts by using object or object part annotations inside training images. The
need for expensive object annotations prevents the wide usage of these methods.
Instead, we propose to select useful parts from multi-scale part proposals in
objects, and use them to compute a global image representation for
categorization. This is specially designed for the annotation-free fine-grained
categorization task, because useful parts have shown to play an important role
in existing annotation-dependent works but accurate part detectors can be
hardly acquired. With the proposed image representation, we can further detect
and visualize the key (most discriminative) parts in objects of different
classes. In the experiment, the proposed annotation-free method achieves better
accuracy than that of state-of-the-art annotation-free and most existing
annotation-dependent methods on two challenging datasets, which shows that it
is not always necessary to use accurate object / part annotations in
fine-grained image categorization.
