The fine-grained image has characteristics of large intra-class variance and small inter-class variance， which makes Fine-Grained Image Categorization （FGIC） much more difficult than traditional image classification tasks. The application scenarios， task difficulties， algorithm development history and related common datasets of FGIC were described， and an overview of related algorithms was mainly presented. Classification methods based on local detection usually use operations of connection， summation and pooling， and the model training was complex and had many limitations in practical applications. Classification methods based on linear features simulated two neural pathways of human vision for recognition and localization respectively， and the classification effect is relatively better. Classification methods based on attention mechanism simulated the mechanism of human observation of external things， scanning the panorama first， and then locking the key attention area and forming the attention focus， and the classification effect was further improved. For the shortcomings of the current research， the next research directions of FGIC were proposed.