Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Parsing clothing in fashion photographs
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Predicting occupation via human clothing and contexts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Who Blocks Who: Simultaneous clothing segmentation for grouping images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi, magic closet, tell me what to wear!
Proceedings of the 20th ACM international conference on Multimedia
Approximate gaussian mixtures for large scale vocabularies
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Describing clothing by semantic attributes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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We present a scalable approach to automatically suggest relevant clothing products, given a single image without metadata. We formulate the problem as cross-scenario retrieval: the query is a real-world image, while the products from online shopping catalogs are usually presented in a clean environment. We divide our approach into two main stages: a) Starting from articulated pose estimation, we segment the person area and cluster promising image regions in order to detect the clothing classes present in the query image. b) We use image retrieval techniques to retrieve visually similar products from each of the detected classes. We achieve clothing detection performance comparable to the state-of-the-art on a very recent annotated dataset, while being more than 50 times faster. Finally, we present a large scale clothing suggestion scenario, where the product database contains over one million products.