A self-attention-based destruction and construction learning fine-grained image classification method for retail product recognition
A self-attention-based destruction and construction learning fine-grained image classification method for retail product recognition
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一种基于自注意力破坏和构建学习的零售产品识别细粒度图像分类方法
DOI：
10.1007/s00521-020-05148-3
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发表时间：
2020-07
影响因子：
6
通讯作者：
Song Deng
中科院分区：
文献类型：
--
作者：
Wenyong Wang;Yongcheng Cui;Guangshun Li;Chuntao Jiang;Song Deng
属于同一类别的零售商品通常具有颜色、形状、尺寸等极其相似的外观特征，无法通过常规分类方法进行区分。目前解决这一问题最有效的方法是细粒度分类方法，即利用机器视觉+场景对目标局部区域进行精细的特征表示，从而实现细粒度分类。细粒度分类方法已广泛用于识别鸟类、汽车、飞机等。然而，现有的细粒度分类方法仍然存在一些缺陷。提出了一种改进的基于自注意破坏与构造学习（SADCL）的零售商品细粒度分类方法。具体而言，该方法利用自注意机制，以端到端的方式对图像信息进行破坏和构建，从而在推理过程中计算出精确的细粒度分类预测和大信息区域。我们在零售产品结账（RPC）数据集上测试了所提出的方法。实验结果表明，该方法在零售商品识别推理中取得了80%以上的准确率，远高于其他细粒度分类方法的结果。
Retail products belonging to the same category usually have extremely similar appearance characteristics such as colors, shapes, and sizes, which cannot be distinguished by conventional classification methods. Currently, the most effective way to solve this problem is fine-grained classification methods, which utilize machine vision + scene to perform fine feature representations on a target local region, thereby achieving fine-grained classification. Fine-grained classification methods have been widely used for recognizing birds, cars, airplanes, and many others. However, the existing fine-grained classification methods still have some drawbacks. In this paper, we propose an improved fine-grained classification method based on self-attention destruction and construction learning (SADCL) for retail product recognition. Specifically, the proposed method utilizes a self-attention mechanism in the destruction and construction of image information in an end-to-end fashion so that to calculate a precise fine-grained classification prediction and large information areas in the reasoning process. We test the proposed method on the Retail Product Checkout (RPC) dataset. Experimental results demonstrate that the proposed method achieved an accuracy above 80% in retail commodity recognition reasoning, which is much higher than the results of other fine-grained classification methods.
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DOI：
10.1109/cvpr.2019.00515
发表时间：
2019-03
期刊：
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
--
作者：
Heliang Zheng;Jianlong Fu;Zhengjun Zha;Jiebo Luo
通讯作者：
Heliang Zheng;Jianlong Fu;Zhengjun Zha;Jiebo Luo
DOI：
--
发表时间：
2017-05
期刊：
ArXiv
影响因子：
--
作者：
Yuichi Yoshida;Takeru Miyato
通讯作者：
Yuichi Yoshida;Takeru Miyato
DOI：
--
发表时间：
2011-07
期刊：
--
影响因子：
--
作者：
C. Wah;Steve Branson;P. Welinder;P. Perona;Serge J. Belongie
通讯作者：
C. Wah;Steve Branson;P. Welinder;P. Perona;Serge J. Belongie
DOI：
--
发表时间：
2013-06
期刊：
ArXiv
影响因子：
--
作者：
Subhransu Maji;Esa Rahtu;Juho Kannala;Matthew B. Blaschko;A. Vedaldi
通讯作者：
Subhransu Maji;Esa Rahtu;Juho Kannala;Matthew B. Blaschko;A. Vedaldi
用于细粒度图像分类的对象部分注意力模型
影响因子：
10.6
作者：
Peng, Yuxin;He, Xiangteng;Zhao, Junjie
通讯作者：
Zhao, Junjie