MACTEN:新型大规模布料纹理分类框架

doi:10.11896/jsjkx.191200115

摘要/Abstract

摘要： 针对纺织品行业布料种类数多且纹理复杂导致人为区分困难的问题,引入深度学习技术,提出了融合多尺度注意力共现特征的残差纹理编码网络模型(MACTEN),并基于此实现Web端大规模布料分类系统。MACTEN主要包含注意力共现表示模块(ACM),改进的残差编码模块(REM),以及多尺度纹理编码融合模块(MTEM)。ACM使用注意力机制对不同类型的布料自适应调整纹理共现特征权重,并通过扩展共现域优化共现特征的联合分布,形成更精致的纹理共现特征;REM通过了字典学习方式,产生改进的残差编码,包含空间不变性的全局纹理信息,有效解决了布料纹理的无序表示问题。最后,MTEM同时融合多个尺度注意力纹理共现特征与级联残差纹理编码作为描述子,可以表示不同形状大小的无序布料纹理。在自建布料数据集上,MACTEN相比几种基线算法有更好的表现。此外,KTHTIPS,FMD,DTD数据集的实验结果表明,MACTEN能够泛化作为通用纹理分类算法。

关键词: 布料分类, 共现特征, 深度学习, 特征融合, 纹理描述子

Abstract: The miscellany of fabric and the complexity of its texture have been viewed as enormous challenges to distinguish artificially.The merging multi-scale attention co-occurrence representation's residual texture encoding network(MACTEN) has been proposed with the introduction of the deep learning technology.And based on that,the large-scale fabric classification system on the web has been carried out.The MACTEN mainly composed of attention co-occurrence representation module (ACM) and improved residual coding module (REM),as well as multi-scale texture coding fusion module (MTEM).In this work,the mechanism of attention has been implemented into ACM to deal with different types of clothes,which adaptively adjusts the weight of texture co-occurrence features,and optimizes the joint distribution of co-occurrence features by expanding the co-occurrence domain to form more refined texture co-occurrence features.Moreover,the improved residual coding,including global texture information of spatial invariance,has been obtained with introduction of dictionary learning method into REM,which can solve the problem of disordered representation of cloth texture effectively.Finally,MTEM combined multiple scale attention texture co-occurrence features and cascaded residual texture coding as descriptors,can represent different shape and size of disordered fabric texture.On self-building cloth dataset,MACTEN has exhibited better performance than other baseline algorithms.Furthermore,the experimental results of KTHTIPS,FMD and DTD datasets show that MACTEN can be generalized as a general texture classification algorithm.

Key words: Cloth classification, Co-occurrence feature, Deep learning, Feature fusion, Texture descriptors

中图分类号:

TP391

李浩翔, 李浩君. MACTEN:新型大规模布料纹理分类框架[J]. 计算机科学, 2020, 47(11A): 258-265. https://doi.org/10.11896/jsjkx.191200115

LI Hao-xiang, LI Hao-jun. MACTEN:Novel Large Scale Cloth Texture Classification Architecture[J]. Computer Science, 2020, 47(11A): 258-265. https://doi.org/10.11896/jsjkx.191200115

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