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Image fine-grained recognition method based on reinforcement learning strategy

The invention provides a fine-grained recognition method based on reinforcement learning and cross bilinear features, aiming at solving the problem that an area with the best discrimination capabilityof a fine-grained image is difficult to mine. An actor-Critic strategy is used to mine the most attention-grabbing areas of an image. An Actor module is responsible for generating top M candidate areas with the best discrimination capability. A Critic module evaluates the state value of the action by utilizing the cross bilinear characteristic; and then calculates a reward value of the action under the current state by utilizing a sorting one-type reward, further obtains a value advantage, feeds the value advantage back to the Actor module, updates the output of the region with the most attention, and finally predicts the fine-grained category by using the region with the most discrimination capability in combination with the original image characteristics. According to the method, the region with the most attention of the fine-grained image can be better mined. It is verified by experiments that the recognition accuracy of the present invention on the CUB-200-2011 public data set isimproved compared with the existing methods, and the high fine-grain recognition accuracy rate is achieved respectively.
Owner:SOUTHEAST UNIV
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