利用掩模RCNN识别单图像服装风格

Lu Wang, Diming Zhang, Yuanjiang Li, Zhenxing Li
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引用次数: 0

摘要

来自社交媒体的视觉时尚风格识别是在线营销的关键应用。目前,这些都是完全手工完成的,效率很低。我们的目标是用人工智能来解决这个问题。本文提出了一种基于Mask RCNN的服装风格识别算法。Mask RCNN作为目前最先进的卷积神经网络,在图像处理的各个方面都取得了很大的进步。本文还提供了一个包含五种不同风格的服装数据集。经过迭代训练,模型的最终MAP达到0.79,可以满足高精度服装识别的要求。为了实现深度学习训练和测试,我们还提出了一个时尚风格数据集。实验结果也证明了Mask RCNN在服装风格识别中的优异性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single Image Clothing Style Recognition Using Mask RCNN
Visual fashion style recognition from social media is a key application for online marketing. Currently, such is achieved fully manually, which is ineffectively. We aim to solve it using artificial intelligence. In this paper, a clothing style recognition algorithm based on Mask RCNN is proposed. As the most advanced convolutional neural network, Mask RCNN has made great progress in all aspects of image processing. The paper also contributes a clothing dataset with five different styles. After iterative training, the final MAP of the model reaches 0.79, which can meet the requirements of high-precision clothing recognition. To enable deep learning training and testing, we also propose a fashion style dataset. The experimental results also demonstrate the excellent performance of Mask RCNN in clothing style recognition.
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