北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (2): 104-108.

• 模式识别与图像处理 • 上一篇    下一篇

基于多层判别字典学习的传统服饰图像分类算法

赵海英1,2,王梓舟3   

  1. 1. 北京邮电大学世纪学院移动媒体与文化计算北京市重点实验室
    2. 北京邮电大学数字媒体与设计艺术学院
    3. 北京邮电大学
  • 收稿日期:2022-04-27 修回日期:2022-08-15 出版日期:2023-04-28 发布日期:2023-05-14
  • 通讯作者: 赵海英 E-mail:zhaohaiying@bupt.edu.cn
  • 基金资助:
    基于5G冬奥赛事和中国文化多语种全球传播服务平台研发;面向文化计算的PatternNet标签语义空间构建研究

Traditional clothing image classification algorithm based on multi-layer discriminant dictionary learning

  • Received:2022-04-27 Revised:2022-08-15 Online:2023-04-28 Published:2023-05-14

摘要: 多层判别式字典学习在图像分类方面已有显著的效果。然而,现有的多层判别式字典学习大多采用交替方向乘子法实现字典的更新,当图像内容比较丰富且含有多个标签时,在多标签分类上的表现不佳。通过递归最小二乘法与去相关增强重建系数算法构成的二层判别式字典学习结构更加适合用于图像多标签分类。通过多层判别式字典学习对数据进行多次稀疏分解,在最后一层用线性分类器对稀疏分解得到的特征向量进行分类。在明清服饰纹样数据集上的实验结果验证了本文算法的优越性,相比现有最新算法,分类精度达到82.17%,取得了同类算法中最优效果。

关键词: RLS-DLA, ODL, Feature Sign Search, 图像分类, 监督学习, 字典学习, 稀疏表征

Abstract: Multi-layer discriminant dictionary learning has achieved remarkable results in image classification. However, the existing multi-layer discriminant dictionary learning mostly uses the alternating direction multiplier method to update the dictionary. When the image content is rich and contains multiple tags, it performs poorly in multi tag classification. The two-layer discriminant dictionary learning structure composed of recursive least square method and decorrelation enhancement reconstruction coefficient algorithm is more suitable for image multi label classification. The data is sparse decomposed many times through multi-layer discriminant dictionary learning, and the feature vectors obtained by sparse decomposition are classified by linear classifier in the last layer. The experimental results on the dress pattern data set of the Ming and Qing Dynasties verify the superiority of this algorithm. Compared with the latest existing algorithm, the classification accuracy reaches 82.17%, which achieves the best effect in similar algorithms.

Key words: RLS-DLA, ODL, Feature Sign Search, Image Classification, Supervise Learning, Dictionary Learning, Sparse Representation

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