基于卷积神经网络的灯具商品图像检索
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引用本文:邰瑶,陈健美.基于卷积神经网络的灯具商品图像检索[J].计算技术与自动化,2019,(4):113-116
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作者单位
邰瑶,陈健美 (江苏大学 计算机科学与通信工程学院江苏 镇江212000) 
中文摘要:建材商品数字化对有效使用电商平台家居资源具有重要意义,传统分类法未考虑主观特性且大部分特征需人工提取,存在细节特征丢失等问题。提出了一种基于卷积神经网络(简称CNN)的灯具图像分类法,并通过一系列预处理操作丰富数据集,提高图像识别率。检索过程结合卷积层和全连接层特征并融合YOLO算法完成复杂的标签分类任务,效果更加高效准确。
中文关键词:卷积神经网络  商品图片搜索  YOLO算法  多标签分类任务
 
Research on Lighting Product Image Based on CNN
Abstract:The digitization of building materials is of great significance for the effective use of e-commerce platform home resources. The traditional classification method does not consider subjective characteristics and most of the features need to be manually extracted,and there are problems such as loss of detail features. A convolutional neural network is proposed. (CNN for short) luminaire image classification method,and improve image recognition rate by a series of pre-processing operation rich data sets . The retrieval process combines the convolutional layer and the fully connected layer features and incorporates the YOLO algorithm to complete the complex label classification task,and the effect is more efficient and accurate.
keywords:convolutional neural network  commodity image search  YOLO algorithm  multi-label classification task
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