[1]SONG Siyu,MIAO Duoqian.Fine-grained image classification algorithm based on multi-granularity regions shuffle[J].CAAI Transactions on Intelligent Systems,2022,17(1):144-150.[doi:10.11992/tis.202105040]
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Fine-grained image classification algorithm based on multi-granularity regions shuffle

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 144-150 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-01-05
Title:
Fine-grained image classification algorithm based on multi-granularity regions shuffle
Author(s):
SONG Siyu1;  MIAO Duoqian1; 2
1. College of Computer Science and Technology, Tongji University, Shanghai 201804, China;
2. Key Laboratory of Embedded System and Service Computing Ministry of Education, Tongji University, Shanghai 201804, China
Keywords:
fine-grained image classification; regions shuffle; multi-granularity; deep learning; data augmentation; convolutional neural network; weakly-supervised learning; local areas
CLC:
TP183
DOI:
10.11992/tis.202105040
Abstract:
Fine-grained image classification is a challenging task of computer vision due to the high application value in many reality scenes, having high value in actual application scenarios. The differences in the overall outline of objects from different sub-categories are slight, and the delicate, discriminative local regions are the key to improve the classification accuracy. However, these essential local areas may have different scales, which cannot be measured by a single criterion. Therefore, a multi-granularity regions shuffle module is proposed to help the neural network learn how to find discriminative details for different scales. The module would first divide the image into local areas with different granularity, and then these regions will be shuffled and reorganized to form a new image, which will also be inputted to the network. The irrelevance among regions of the new image forces the network to find discriminative local regions under different granularity and learn from regions. Experimental results of three datasets widely used as benchmarks in fine-grained image classification show that the proposed module can effectively help the network find discriminative local regions and achieve excellent performance with no additional information required to be marked on any part of the image.
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