应用科学学报 ›› 2023, Vol. 41 ›› Issue (6): 967-977.doi: 10.3969/j.issn.0255-8297.2023.06.005
周啸辉, 余磊, 张睿婷, 熊邦书, 欧巧凤
收稿日期:2021-12-03
出版日期:2023-11-30
发布日期:2023-11-30
通信作者:
余磊,副教授,研究方向为图像处理及应用。E-mail:yulei@nchu.edu.cn
E-mail:yulei@nchu.edu.cn
基金资助:ZHOU Xiaohui, YU Lei, ZHANG Ruiting, XIONG Bangshu, OU Qiaofeng
Received:2021-12-03
Online:2023-11-30
Published:2023-11-30
摘要: 服装图像具有明暗不一、尺度各异的特性,这使得已有识别方法表现不佳。为解决此问题,本文基于空间注意力选择核(space attention selective kernel,SASK)模块和双分支结构搭建神经网络模型对服装图像进行识别。首先,结合跳跃连接、稠密连接和多尺度、通道拆分的思想,设计双分支神经网络,充分提取服装对象的整体特征。其次,基于空间注意力机制,设计SASK模块,使网络可以更多地关注服装对象的形态特征信息,从而提升识别效果。实验结果表明,本文所提方法不但在典型服装数据集上能够取得优于现有主流方法的识别精度,而且在具有明暗不一、尺度各异特性的其他图像数据集上同样表现良好。
中图分类号:
周啸辉, 余磊, 张睿婷, 熊邦书, 欧巧凤. 基于SASK和双分支结构的服装图像识别方法[J]. 应用科学学报, 2023, 41(6): 967-977.
ZHOU Xiaohui, YU Lei, ZHANG Ruiting, XIONG Bangshu, OU Qiaofeng. Clothing Image Recognition Method Based on SASK and Double Branch Structure[J]. Journal of Applied Sciences, 2023, 41(6): 967-977.
| [1] HuAnG Q B, HAn X P, Lu T, et al. Clothing image retrieval based on parts detection and segmentation[C]//20213rd International Conference on Image Processing and Machine Vision(IPMV), 2021:53-59. [2] 赵宏伟,刘晓涵,张媛,等.基于关键点注意力和通道注意力的服装分类算法[J].吉林大学学报(工学版), 2020, 50(5):1765-1770.ZhAo H W, Liu X H, ZhAnG Y, et al. Clothing classification algorithm based on landmark attention and channel attention[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5):1765-1770.(in Chinese) [3] 李巨虎,范睿先,陈志泊.基于颜色和纹理特征的森林火灾图像识别[J].华南理工大学学报(自然科学版), 2020, 48(1):70-83.Li J H, FAn R X, ChEn Z B. Forest fire recognition based on color and texture features[J].Journal of South China University of Technology(Natural Science Edition), 2020, 48(1):70-83.(in Chinese) [4] 郑长亮,庞明.基于卷积神经网络的时空权重姿态运动特征提取算法[J].应用科学学报, 2021,39(4):594-604.ZhEnG C L, PAnG M. Spatial-temporal weight attitude motion feature extraction algorithm using convolutional neural network[J]. Journal of Applied Sciences, 2021, 39(4):594-604.(in Chinese) [5] 赵云山,段友祥.基于Attention机制的卷积神经网络文本分类模型[J].应用科学学报, 2019, 37(4):541-550.ZhAo Y S, DuAn Y X. Convolutional neural networks text classification model based on attention mechanism[J]. Journal of Applied Sciences, 2019, 37(4):541-550.(in Chinese) [6] 彭宁,陈爱斌,周国雄,等.基于正弦注意力表征网络的环境声音识别[J].应用科学学报, 2021,39(4):641-649.PEnG N, ChEn A B, Zhou G X, et al. Environmental sound recognition based on attention sinusoidal representation network[J].Journal of Applied Sciences, 2021, 39(4):641-649.(in Chinese) [7] 陈巧红,陈翊,李文书,等.多尺度SE-Xception服装图像分类[J].浙江大学学报(工学版), 2020,54(9):1727-1735.ChEn Q H, ChEn Y, Li W S, et al. Clothing image classification based on multi-scale SEXception[J]. Journal of Zhejiang University(Engineering Science), 2020, 54(9):1727-1735.(in Chinese) [8] ELLEuch M, MEzGhAni A, KhEMAkhEM M, et al. Clothing classification using deep CNN architecture based on transfer learning[C]//International Conference on Hybrid Intelligent Systems. Cham:Springer, 2021:240-248. [9] Ai X Z, ZhuAnG J W, WAnG Y H, et al. ResCaps:an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma[J]. Complex&Intelligent Systems, 2022, 8(3):1865-1873. [10] Guo Y C, Du L, ChEn J. Max-margin multi-scale convolutional factor analysis model with application to image classification[J]. Expert Systems with Applications, 2019, 133:21-33. [11] SEo Y, Shin K S. Hierarchical convolutional neural networks for fashion image classification[J]. Expert Systems with Applications, 2019, 116:328-339. [12] HuAnG W K, Zhou F B. DA-CapsNet:dual attention mechanism capsule network[J]. Scientific Reports, 2020, 10(1):1-13. [13] Li X, WAnG W H, Hu X L, et al. Selective kernel networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 2019:510-519. [14] ChEn J B, HuAnG R Y, ZhAo K, et al. Multiscale convolutional neural network with feature alignment for bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70:1-10. [15] Li J, Liu Y, ZhAnG Y D, et al. Cascaded attention DenseUNet(CADUNet)for road extraction from very-high-resolution images[J]. ISPRS International Journal of Geo-Information, 2021,10(5):329. [16] HuAnG G, Liu Z, VAn DER MAATEn L, et al. Densely connected convolutional networks[C]//2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 2017:2261-2269. [17] PRAJApATi K, ChuDAsAMA V, PATEL H, et al. Channel split convolutional neural network(ChaSNet)for thermal image super-resolution[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2021:4363-4372. [18] Li R F, Lu W C, LiAnG H Y, et al. Multiple features with extreme learning machines for clothing image recognition[J]. IEEE Access, 2018, 6:36283-36294. [19] AL BATAinEh A, KAuR D. Optimal convolutional neural network architecture design using clonal selection algorithm[J]. International Journal of Machine Learning and Computing, 2019,9(6):788-794. [20] MA B T, Li X, XiA Y, et al. Autonomous deep learning:a genetic DCNN designer for image classification[J]. Neurocomputing, 2020, 379:152-161. [21] BALDoMinos A, SAEz Y, IsAsi P. Hybridizing evolutionary computation and deep neural networks:an approach to handwriting recognition using committees and transfer learning[J].Complexity, 2019:1-16. [22] Dos SAnTos M M, DA SiLvA FiLho A G, Dos SAnTos W P. Deep convolutional extreme learning machines:Filters combination and error model validation[J]. Neurocomputing, 2019,329:359-369. |
| [1] | 熊娟, 张孙杰, 阚亚亚, 陈家豪. 基于CAFPN和细化双头解耦的遥感图像目标检测[J]. 应用科学学报, 2023, 41(6): 989-1003. |
| [2] | 李伟汉, 侯北平, 胡飞阳, 朱必宏. 阿尔茨海默症的多模态分类方法[J]. 应用科学学报, 2023, 41(6): 1004-1018. |
| [3] | 阚亚亚, 张孙杰, 熊娟, 祖奕. 结合transformer多尺度实例交互的稀疏集目标检测[J]. 应用科学学报, 2023, 41(5): 777-788. |
| [4] | 王辉, 丁铂栩. 三维点云表示的人体动作序列预测[J]. 应用科学学报, 2023, 41(3): 461-475. |
| [5] | 萧晓彤, 丁建伟, 张琪. 基于局部和全局梯度上升的分段后门防御[J]. 应用科学学报, 2023, 41(2): 218-227. |
| [6] | 徐增敏, 陆光建, 陈俊彦, 陈金龙, 丁勇. 基于通道特征聚合的行人重识别算法[J]. 应用科学学报, 2023, 41(1): 107-120. |
| [7] | 邹倩颖, 陈晖阳, 李永生, 胡力雯, 王小芳. 粒子群优化的深海图像暗边缘检测优化算法[J]. 应用科学学报, 2023, 41(1): 153-169. |
| [8] | 聂江华, 肖永生, 黄丽贞, 贺丰收. 基于时频分析与深度学习的高分辨距离像雷达目标识别[J]. 应用科学学报, 2022, 40(6): 973-983. |
| [9] | 陈茂霖, 张昕怡, 刘祥江, 姬翠翠, 赵立都. 使用随机邻域分析的地面激光扫描点云采样间隔估算[J]. 应用科学学报, 2022, 40(6): 984-995. |
| [10] | 况发, 熊邦书, 欧巧凤, 余磊. 基于广度残差与像素点注意力的图像去模糊模型[J]. 应用科学学报, 2022, 40(6): 996-1005. |
| [11] | 刘硕, 瞿崇晓, 祝中科, 张福俊, 范长军. 基于MSR和AMSR的红外融合增强算法[J]. 应用科学学报, 2022, 40(3): 423-433. |
| [12] | 孙维亚, 王达, 许帅, 汪京晔, 马占宇. 基于计算机视觉的水位检测算法[J]. 应用科学学报, 2022, 40(3): 434-447. |
| [13] | 张育斌, 陈锋, 乐娟, 程起有. 直升机桨叶图像中圆形标记点圆心检测及修正方法[J]. 应用科学学报, 2022, 40(2): 212-223. |
| [14] | 倪翠, 王朋, 孙浩, 李倩. 一种基于四叉树划分的改进ORB算法[J]. 应用科学学报, 2022, 40(2): 266-278. |
| [15] | 陈丽芳, 魏梦如. 基于强化局部特征的3D点云分类与分割网络[J]. 应用科学学报, 2022, 40(2): 328-337. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||