LAUSR

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 the main 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 how to dress in an aesthetically pleasing manner for a journey.