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Computer vision based fine-grained recognition has received great attentionin recent years. Existing works focus on discriminative part localization andfeature learning. In this paper, to improve the performance of fine-grainedrecognition, we try to precisely locate as many salient parts of object aspossible at first. Then, we figure out the classification probability that canbe obtained by using separate parts for object classification. Finally, throughextracting efficient features from each part and combining them, then feedingto a classifier for recognition, an improved accuracy over state-of-artalgorithms has been obtained on CUB200-2011 bird dataset.

Fine-grained visual recognition with salient feature detection