  Computer vision based fine-grained recognition has received great attention
in recent years. Existing works focus on discriminative part localization and
feature learning. In this paper, to improve the performance of fine-grained
recognition, we try to precisely locate as many salient parts of object as
possible at first. Then, we figure out the classification probability that can
be obtained by using separate parts for object classification. Finally, through
extracting efficient features from each part and combining them, then feeding
to a classifier for recognition, an improved accuracy over state-of-art
algorithms has been obtained on CUB200-2011 bird dataset.
