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Title
Abstract
Figures
Introduction
Related Work
Data Sets
Qualitative Results
Conclusion
References

Apparel Classification with Style

Profile image of Christian LeistnerChristian Leistner

2012

https://doi.org/10.1007/978-3-642-37447-0_25
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14 pages

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Abstract

Abstract. 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.

Figures (9)
Fig. 1: Overview of our classification pipeline. First, an upper body detection algorithm is applied to the image. Then we densely extract a number of features. Histograms over the extracted features are used as input for a Random Forest (type classification) and for SVMs (attribute classification). Clothing serves for much more than covering and protection. It is a means of communication to reflect social status, lifestyle, or membership of a particular
Fig. 1: Overview of our classification pipeline. First, an upper body detection algorithm is applied to the image. Then we densely extract a number of features. Histograms over the extracted features are used as input for a Random Forest (type classification) and for SVMs (attribute classification). Clothing serves for much more than covering and protection. It is a means of communication to reflect social status, lifestyle, or membership of a particular
Table 1: List of attribute categories and the attributes therein. Apparel Classification with Style
Table 1: List of attribute categories and the attributes therein. Apparel Classification with Style
Table 2: Main classes and number of images per class of the benchmark data set
Table 2: Main classes and number of images per class of the benchmark data set
Fig. 2: Confusion matrix of our clothing classes for the best performing SVM classifier on the left side and the proposed Transfer Forest on the right side.
Fig. 2: Confusion matrix of our clothing classes for the best performing SVM classifier on the left side and the proposed Transfer Forest on the right side.
Fig. 3: Percentage of co-occurring classes in the deepest split nodes. Note how semantic similar classes often occur to- gether. Table 3: Classification performance mea- sured as average accuracy over all classes on our benchmark data set for different methods.
Fig. 3: Percentage of co-occurring classes in the deepest split nodes. Note how semantic similar classes often occur to- gether. Table 3: Classification performance mea- sured as average accuracy over all classes on our benchmark data set for different methods.
Table 4: Best average accuracy for each attribute category with the correspond- ing features. The number of attributes per category is denoted in in parentheses.
Table 4: Best average accuracy for each attribute category with the correspond- ing features. The number of attributes per category is denoted in in parentheses.
Fig. 4: Some example output of our pipeline. The header denotes the ground truth class. Each example shows the detected bounding box and the output of the type classifier. On the left side of each example, the output of the most confident attribute classifier for each attribute group is shown.
Fig. 4: Some example output of our pipeline. The header denotes the ground truth class. Each example shows the detected bounding box and the output of the type classifier. On the left side of each example, the output of the most confident attribute classifier for each attribute group is shown.

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