The invention belongs to the technical field of computer vision. The invention discloses a fine-grained image recognition algorithm of a Gaussian mixture model based on discriminative feature guidance. The algorithm comprises 1) a low-rank representation mechanism which learns a group of low-rank discrimination bases through the Gaussian mixture model so as to accurately select discrimination details and filter out more irrelevant information on a high-level semantic feature map, and 2) a low-rank representation recombination mechanism which can recover spatial information of the low-rank discrimination bases so as to reconstruct a low-rank feature map. By restoring the low-rank discriminant basis to the same embedding space of the high-level feature map, the LR2M alleviates the diffusionproblem of the discriminant region in the high-level feature map, and the discriminant region can be more accurately positioned on the new low-rank feature map. The DF-GMM is in a CUB-Bird data set, aStanford-Cards data set and an FGVC Aircraft data set, and can obtain the best performance under the same setting as some methods which are most competitive at present.
Fine-grained image recognition algorithm based on discriminative feature-oriented Gaussian mixture model