Abstract: The goal of fine-grained image recognition is to classify object subclasses at a fine-grained level.Because the differences between different subclasses are very subtle,fine-grained image recognition is very challenging.At present,the difficulty of this kind of algorithm is how to locate the distinguishable parts of fine-grained targets and how to extract fine-grained features of fine-grained levels.To this end,a fine-grained recognition method combining Non-local and multi-regional attention mechanisms is proposed.Navigatoronly uses image labels to locate some discriminative regions,and achieves good classification results by fusing global features and discriminative regional features.However,Navigator is still flawed.Firstly,the navigator does not consider the relationship between different locations,so the algorithm proposed in this paper combines the non-local module with the navigator to enhance the global information perception ability of the model.Secondly,aiming at the defect that the Non-local module does not establish the relationship between feature channels,a feature extraction network based on channel attention mechanism is constructed,which makes the network pay more attention to the important feature channels.Finally,the algorithm proposed in this paper achieves recognition accuracy of 88.1%,94.3% and 91.8% on three open fine-grained image databases,CUB-200-2011,Stanford Cars and FGVC Aircraft respectively,and has a significant improvement over Navigator.

Key words: Fine-grained image recognition, Attention mechanism, Non-local, Regional location, Feature extraction