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Apparel classification with style
2D Classification
许可协议: Unknown

Overview

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.

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Apparel classification with style
2D Classification
许可协议: Unknown

Overview

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.

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