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Selected Readings in Vision and Graphics
edited by Luc Van Gool, Gábor Székely, Markus Gross, Bernt Schiele
Efficient Multi-Class Object Detection
2009. XX, 252 pages. EUR 64,00.
Multi-class object detection plays an important role
in semantic image analysis. Usually, the task is solved by running a separate
detector for each class, which quickly gets expensive if the number of classes
increases. In this thesis, we propose a method to improve the efficiency of
multi-class detection by learning shared features between classes. First, two
decision tree approaches are investigated. Then, a better solution is
developed, the so-called 'shared cascade'. Experiments on real-world data show
a significant efficiency improvement compared to standard approches.
Furthermore, we propose a method to add a class to an existing shared cascade
without training everything from scratch again.
About the author:
Philipp Zehnder studied Information Technology and Electrical Engineering at the ETH Zürich where he graduated as Dipl. El.-Ing. ETH in 2002. Subsequently, he joined the Computer Vision Laboratory at the ETH Zürich where he worked as a research assistant and PhD student. In 2009, he was awarded a PhD degree (Dr. sc. ETH Zürich) for his work about efficient multi-class object detection.
Keywords / Schlagwörter:
Object Detection, Multi-Class,
Feature Sharing, Machine Learning, SVM, AdaBoost, Haar Features, Cascade of
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