Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Composite Templates for Cloth Modeling and Sketching
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An empirical evaluation of supervised learning in high dimensions
Proceedings of the 25th international conference on Machine learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Clothes search in consumer photos via color matching and attribute learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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
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We introduce a complete pipeline for recognizing and classifying people's clothing in natural scenes. This has several interesting applications, including e-commerce, event and activity recognition, online advertising, etc. The stages of the pipeline combine a number of state-of-the-art building blocks such as upper body detectors, various feature channels and visual attributes. The core of our method consists of a multi-class learner based on a Random Forest that uses strong discriminative learners as decision nodes. To make the pipeline as automatic as possible we also integrate automatically crawled training data from the web in the learning process. Typically, multi-class learning benefits from more labeled data. Because the crawled data may be noisy and contain images unrelated to our task, we extend Random Forests to be capable of transfer learning from different domains. For evaluation, we define 15 clothing classes and introduce a benchmark data set for the clothing classification task consisting of over 80,000 images, which we make publicly available. We report experimental results, where our classifier outperforms an SVM baseline with 41.38 % vs 35.07 % average accuracy on challenging benchmark data.