Fine-Grained Visual Categorization And Application Based On Deep Learning

In recent years,deep learning has received more and more attention and research,promoting practical application in many fields about image recognition at the same time.This paper based on deep learning,researched its application in fine-grained visual categorization,including fine-grained biology image recognition and person re-identification.Fine-grained biology image recognition classifies image according to biology object in it;Person re-identification is the base of video monitoring,and it aims to match person images from different cameras.There are few differences in person images,so distinguishing them can be considered the task of generalized fine-grained visual categorization.This paper is divided into following two sections.1?This paper proposed one hierarchical classification network to recognize biology images.Different species correspond to different levels in biological taxonomy,and we can organize them in a tree structure.Because previous methods almost neglected hierarchical information between different species and only classified in the lowest level.For this problem,this paper proposed one hierarchical classification network.This network classifies from top to bottom,and every sub-classifier only classifies species having the same father node.The differences between species recognized by lower-level classifier is fewer,so the classifier can learn finer feature.The results of experiment show,compared to the flat classification network neglecting the information between different species,the computation of hierarchical classification network did not increase,but the classification accuracy was improved.2?For person re-identification,this paper applied compact bilinear convolutional neural network and merged finer local features into global feature.Bilinear convolutional neural network was initially proposed to handle fine-grained visual categorization problem,and the bilinear vector extracted by it is usually of high dimension,so it is not convenient for follow-up computation.To tackle this problem,this paper applied compact method to reduce feature's dimension.For the situation that feature maps from main network are small and they cannot preserve person local features fully,we added extra shallow sub-network to get finer features and merged them to global features from main network.As object function,this paper chose histogram loss,because this function has higher data utilization and avoids setting parameters of the ordinary loss function in person re-identification.The result of experiment showed,this paper's method improved performance on related methods.