基于视觉的服装属性分类算法

刘聪; 丁贵广

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基于视觉的服装属性分类算法

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- 基于视觉的服装属性分类算法
- Visual-Based Clothing Attribute Classification Algorithm
- 微电子学与计算机 2016年33卷第1期页码：28-33
- 作者机构：
  
  1. 清华大学计算机科学与技术系,北京,100084
  2. 清华大学软件学院,北京,100084
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61271394
- DOI：
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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刘聪, 丁贵广. 基于视觉的服装属性分类算法[J]. 微电子学与计算机, 2016, 33(1): 28-33.

LIU Cong, DING Gui-guang. Visual-Based Clothing Attribute Classification Algorithm[J]. 2016, 33(1): 28-33.
刘聪, 丁贵广. 基于视觉的服装属性分类算法[J]. 微电子学与计算机, 2016, 33(1): 28-33. DOI：

LIU Cong, DING Gui-guang. Visual-Based Clothing Attribute Classification Algorithm[J]. 2016, 33(1): 28-33. DOI：

摘要

提出了一种服装图像属性分类算法.针对服装图像噪声多的问题

采用人体部位检测技术定位服装关键部位并去除冗余信息

提高了属性分类的准确率.并提出了一种基于人体骨架与皮肤的特征提取算法

以较少的维数表达衣型特点

显著加快相关属性的分类速度.针对服装属性语义复杂、需求多样化的问题

为不同的属性构建了不同的SVM决策树模型

从而提高分类效率

并同时满足粗、细粒度的服装分类需求.实验结果验证了该方法在多种服装属性分类任务上的有效性.

Abstract

We propose an algorithm for classifying clothing image attributes. To handle the noise in clothing images

key parts of clothing are located by a well-trained human part detector

and redundant information is eliminated

by which means the accuracy of clothing attribute classification is improved. Additionally

a novel feature descriptor based on human skeleton and skin is also proposed. This descriptor describes clothing feature with fewer dimensions

which significantly speeds up classifiers of related attributes. To deal with the complex semantic of clothing attributes

different SVM Decision Tree models are built for different attributes

which improves the efficiency of classification and achieves the objective of both coarse-grained and fine-grained classification. Experiments demonstrate the effectiveness of the proposed algorithm on multiple clothing attribute classification tasks.

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references

Farhadi A, Endres I, Hoiem D, et al. Describing objects by their attributes[C]//Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. USA, Miami, IEEE, 2009: 1778-1785.

Yang Y, Ramanan D. Articulated pose estimation with flexible mixtures-of-parts[C]//Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. USA, Colorado Springs, IEEE, 2011: 1385-1392.

Liu S, Feng J, Song Z, et al. Hi, magic closet, tell me what to wear![C]//Proceedings of the 20th ACM international conference on Multimedia. USA, Dallas, ACM, 2012: 619-628.

Madzarov G, Gjorgjevikj D, Chorbev I. A Multi-class SVM Classifier Utilizing Binary Decision Tree[J]. Informatica (Slovenia), 2009, 33(2): 225-233. DOI: 225

Bourdev L, Maji S, Brox T, et al. Detecting people using mutually consistent poselet activations[M]//Computer Vision-ECCV 2010. Berlin, Heidelberg Springer, 2010: 168-181.

Jones M J, Rehg J M. Statistical color models with application to skin detection[J]. International Journal of Computer Vision, 2002, 46(1): 81-96. DOI: 81

徐勋华, 王继成. 支撑向量机的多类分类方法[J]. 微电子学与计算机, 2005, 21(10): 149-152. DOI: 149

张学工. 关于统计学习理论与支持向量机[J]. 自动化学报, 2000, 26(1): 32-42. DOI: 32

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