计算机工程2016，Vol.42Issue(11)：309-315,7.DOI:10.3969/j.issn.1000-3428.2016.11.053
基于深度卷积神经网络的服装图像分类检索算法
Clothing Image Classification and Retrieval Algorithm Based on Deep Convolutional Neural Network
摘要
Abstract
Aiming at the problem that clothing image retrieval algorithm based on deep learning has low classification accuracy,this paper proposes an improved clothing image classification and retrieval algorithm based on Deep Convolutional Neural Network(DCNN).A clothing image database that contains 100 000 images with 16 attributes, called B_DAT Clothing,is established.Duo to the complex performance of clothing images,it uses DCNN to learn the features adaptively from the B_DAT Clothing Database,design the hash index of CNN’s features for building an efficient attribute-based retrieval model,and realize efficient classification and quick retrieval of Clothing images.Experimental results show that the algorithm can achieve better performance in terms of classification and retrieval than the traditional visual feature classification algorithms.关键词
服装属性/卷积神经网络/属性检索/分类排序/哈希索引/服装数据库Key words
clothing attributes/Convolutional Neural Network (CNN )/attribute retrieval/classification sorting/hash index/clothing database分类
信息技术与安全科学引用本文复制引用
厉智,孙玉宝,王枫,刘青山..基于深度卷积神经网络的服装图像分类检索算法[J].计算机工程,2016,42(11):309-315,7.基金项目
国家自然科学基金（61672292，61300162，61272223）；江苏省自然科学基金（BK20131003）。 （）
