Image classification is an important research branch in the field of computer vision. Ordinary image classification focuses on the discrimination of object differences, while fine-grained image classification focuses on the distinction between different sub-categories. Considering the small differences between sub-categories and the larger differences within sub-categories due to factors such as shooting angle, background, and posture, fine-grained image recognition becomes a challenging task. In order to solve the problem of fine-grained object classification, this paper proposes a finegrained object classification method based on block diagonal feature and ensemble learning. The Gabor feature of the object image is block diagonalized by using low-rank recovery technology, and the low-rank feature representation of the object is constructed by introducing the block diagonal sparse regular term to increase the discriminability of features between object sub-categories. On this basis, the stacking ensemble learning method is used to classify objects, and a fine-grained real animal image and non-real animal image (toy animal image) classification database is constructed. And the algorithm design and experiment are completed. Through the proposed algorithm, accuracy of fine-grained classification of objects is improved
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