Visual-Based Clothing Attribute Classification Algorithm

LIU Cong; DING Gui-guang

Chinese

您当前的位置：

首页 >

文章列表页 >

Visual-Based Clothing Attribute Classification Algorithm

更新时间：2026-09-04

- Visual-Based Clothing Attribute Classification Algorithm
- Vol. 33, Issue 1, Pages: 28-33(2016)
- 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：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
移动端阅览
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.

关键词

Keywords

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

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of ultra-low power real-time clock circuits

Microservices anomaly detection based on the correlation of monitoring metrics

Aerial small object detection algorithm based on improved RT-DETR

A review of large model development: technological evolution from BERT to Deepseek

High gain stability high temperature operational amplifier based on reverse body bias

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰