Trip Outfits Advisor: Location-Oriented Clothing Recommendation
文献类型：期刊论文
| 作者 | Zhang, Xishan2; Jia, Jia4; Gao, Ke2; Zhang, Yongdong2,3; Zhang, Dongming1; Li, Jintao2; Tian, Qi5 |
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2017-11-01 |
| 卷号 | 19期号:11页码:2533-2544 |
| 关键词 | Clothing recommendation fashion analysis location-based multimedia system |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2017.2696825 |
| 英文摘要 | When packing for a journey, have you ever asked "what clothes should I take with me?" Wearing appropriate and aesthetically pleasing clothing when traveling is a concern for many of us. Our data observation of photos from several popular travel websites reveals that people's choice of clothing items and their color combinations have strong correlations with the weather, the season, and themain type of attraction at the destination. This leads to an interesting and novel problem: can the correlation between clothing and locations be automatically learned from social photos and leveraged for location-oriented clothing recommendations? In this paper, we systematically study this problem and propose a hybrid multilabel convolutional neural network combined with the support vector machine (mCNN-SVM) approach to capture the intrinsic and complex correlations between clothing attributes and location attributes. Specifically, we adapt the CNN architecture to multilabel learning and fine-tune it using each fine-grained clothing item. Then, the recognized items are fed to the SVM to learn the correlations. Experiments on three fashion datasets and a benchmark journey outfit dataset show that our proposed approach outperforms several baselines by over 10.52-16.38% in terms of the mAP for clothing item recognition and outperforms several alternative methods by over 9.59-29.41% in terms of the mAP when ranking clothing by appropriateness for travel destinations. Finally, an interesting case study demonstrates the effectiveness of our method by answering what items to wear, how to match them, and howto dress in an aesthetically pleasing manner for a journey. |
| 资助项目 | National Nature Science Foundation of China[61525206] ; National Nature Science Foundation of China[61271428] ; National Nature Science Foundation of China[61672495] ; National Nature Science Foundation of China[61429201] ; National Nature Science Foundation of China[61370023] ; National Nature Science Foundation of China[61521002] ; National Key Research and Development Plan of China[2016YFB0801203] ; National Key Research and Development Plan of China[2016YFB1001200] ; National Key Research and Development Plan of China[2016YFB0801200] ; National Key Research and Development Plan of China[2016IM010200] ; Beijing Advanced Innovation Center for Imaging Technology[BAICIT-2016009] ; ARO[W911NF-15-1-0290] ; NEC Laboratories of America ; Blippar |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000413068200014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/6874]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhang, Yongdong |
| 作者单位 | 1.Coordinat Ctr China, Natl Comp Network Emergency Response Tech Team, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA |
| 推荐引用方式 GB/T 7714 | Zhang, Xishan,Jia, Jia,Gao, Ke,et al. Trip Outfits Advisor: Location-Oriented Clothing Recommendation[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2017,19(11):2533-2544. |
| APA | Zhang, Xishan.,Jia, Jia.,Gao, Ke.,Zhang, Yongdong.,Zhang, Dongming.,...&Tian, Qi.(2017).Trip Outfits Advisor: Location-Oriented Clothing Recommendation.IEEE TRANSACTIONS ON MULTIMEDIA,19(11),2533-2544. |
| MLA | Zhang, Xishan,et al."Trip Outfits Advisor: Location-Oriented Clothing Recommendation".IEEE TRANSACTIONS ON MULTIMEDIA 19.11(2017):2533-2544. |
入库方式： OAI收割
来源：计算技术研究所
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