Information on fabric material is necessary in washing and ironing clothing. However, indication on a care tag may peel off or the tag may come off due to deterioration over time. Discrimination of the material from the fabric itself is not easy for a general person. Estimating the material of an object is one of the challenging tasks in computer vision. This paper deals with the identification of cloth materials using computer vision. We studied a method to discriminate the fabric material from the image of clothing taken by a smartphone camera. First, we investigated the relationship between image resolution and discrimination accuracy using a convolutional neural network (CNN). As a result, we observed that the accuracy changes with resolution and that the resolution at which the accuracy is highest differs depending on the material. Based on these results, we proposed a fabric material discrimination method using multi-resolution images by combining two CNNs. As a result of the evaluation experiment, the proposed method discriminated six kinds of fabric materials with 87.1% accuracy, and the accuracy was significantly higher than that of the comparison method without using multi-resolution images.

中文翻译：

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从智能手机相机图像中辨别服装材质

洗涤和熨烫衣物时需要有关织物材料的信息。但是，随着时间的推移，护理标签上的指示可能会脱落或标签可能会脱落。对于一般人来说，从面料本身来辨别材质并不容易。估计物体的材料是计算机视觉中具有挑战性的任务之一。本文介绍了使用计算机视觉识别布料材料。我们研究了一种从智能手机相机拍摄的服装图像中区分织物材料的方法。首先，我们使用卷积神经网络 (CNN) 研究了图像分辨率与辨别精度之间的关系。结果，我们观察到精度随分辨率而变化，并且精度最高的分辨率因材料而异。基于这些结果，我们提出了一种通过结合两个 CNN 使用多分辨率图像的织物材料识别方法。评价实验结果表明，该方法以87.1%的准确率判别了6种织物材质，准确率明显高于未使用多分辨率图像的对比方法。