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Carnegie Mellon Robotics Institute

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Abstract

This paper presents a new approach to parts-based object detection. Objects are described using a spatial model based on its constituent parts. Unlike most existing methods, parts are discovered in an unsupervised manner from training images with only object bounding boxes provided. The association between parts is modeled using boosted decision trees that allows arbitrary object-part configurations to be maintained. Experimental results on the challenging VOC 2007 dataset validate our approach.

Keywords

Object Detection

Notes

Text Reference

Santosh Kumar Divvala, Charles Zitnick, Ashish Kapoor, and Simon Baker, "Detecting Objects using Unsupervised Parts-based Attributes," tech. report CMU-RI-TR-11-10, Robotics Institute, Carnegie Mellon University, August, 2010

BibTeX Reference

@techreport{Divvala_2010_6837,
   author = "Santosh Kumar Divvala and Charles Zitnick and Ashish Kapoor and Simon Baker",
   title = "Detecting Objects using Unsupervised Parts-based Attributes",
   booktitle = "",
   institution = "Robotics Institute",
   month = "August",
   year = "2010",
   number= "CMU-RI-TR-11-10",
   address= "Pittsburgh, PA",
}

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