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首页> 外文期刊>Journal of visual communication & image representation >Finding suits in images of people in unconstrained environments
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Finding suits in images of people in unconstrained environments

机译：在不受限制的环境中查找人物图像中的西装

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摘要

Clothing style analysis is a critical step for understanding images of people. To automatically identify the style of clothing that people wear is a challenging task due to various poses of person and large variations for even the same clothing category. Suit as one of the clothing style is a key element in many important activities. In this paper, we propose a novel suits detection method for images of people in unconstrained environments. In order to cope with various human poses, human pose estimation is incorporated. By analyzing the style of clothing, we propose the color features, shape features and statistical features for suits detection. Experiments with four popular classifiers have been conducted to demonstrate that the proposed features are effective and robust. Comparative experiments with Bag of Words (BoW) method demonstrate that the proposed features are superior to BoW which is a popular method for object detection. The proposed method has achieved promising performance over our dataset, which is a challenging web image set with various human poses and diverse styles of clothing.
机译：服装风格分析是理解人物形象的关键步骤。由于人的姿势各异，甚至同一服装类别的变化也很大，因此要自动识别人们穿着的服装样式是一项艰巨的任务。西装作为一种服装风格是许多重要活动的关键要素。在本文中，我们提出了一种用于在不受约束的环境中对人的图像进行检测的新方法。为了应付各种人体姿势，结合了人体姿势估计。通过分析服装的风格，我们提出了颜色特征，形状特征和统计特征以用于西服检测。已经对四个流行的分类器进行了实验，以证明所提出的功能是有效且健壮的。单词袋（BoW）方法的比较实验表明，所提出的功能优于BoW，后者是用于对象检测的流行方法。所提出的方法在我们的数据集上取得了令人鼓舞的性能，这是一个具有挑战性的Web图像集，具有各种人体姿势和各种样式的服装。

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