基于深度学习的时尚社交图像内容分析

Seema V. Wazarkar, K. Kotecha, S. Patil, Nidhi Kalra
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引用次数: 0

摘要

社会形象内容分析是时尚分析的重要内容之一。由于社会视觉感知对时尚行业的决策非常有用,因此在时尚行业使用拟议的系统将提升他们的业务。它支持业务增长,帮助减少损失或提供风险的先验知识。由于社交数据的性质,分析社交内容数据是一项具有挑战性的任务。社交内容数据是非结构化的,充满了模糊性。但是,这个数据源非常重要,因为它会不断更新,以便可以使用当前数据进行分析。随着时尚趋势的不断变化,与时尚相关的应用程序需要当前数据。因此,在本文中,卷积神经网络与我们的机器学习方法一起应用于寻找最佳时尚分析方法,其中利用社交媒体来预测时尚风格。使用Softsign和Softplus函数的深度学习方法取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Image Content Analysis for Fashion using Deep Learning
Social image content analysis is one of the important tasks for fashion analysis. Use of proposed system in fashion industries will uplift their business as social visual perception is very useful for the decision making in fashion industries. It supports growth in business and helps in minimizing loss or provides prior knowledge of risks. Analysis of social content data is a challenging task due to the nature of social data. Social content data is unstructured and full of ambiguity. But, this source of data is very important because it keeps updating continuously so that current data is being available for analysis. There is a necessity of current data for applications related to fashion as fashion trends keep changing. Therefore, in this paper convolutional neural networks is applied along with our machine learning approaches to find optimal fashion analyzing approach where social media is utilized to predict fashion style. Deep learning approach with Softsign and Softplus function performed well.
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