The Robotics Institute

Carnegie Mellon Robotics Institute

Menu

• About RI
• People
• Research
• Education
• Careers
• News & Media
• Events

• RI History

• Contact Us

• Maps & Directions

• About this Site

• Faculty

• Adjunct Faculty

• Students

• Postdoctoral Fellows

• Project Staff

• Institute Staff

• Visitors

• Alumni

• All RI Members

• Faculty Research Interests

• Research Centers

• Consortia

• Labs & Groups

• Projects

• Publications

• Academic Programs

• Admissions

• Policies and Forms

• Courses

• Student Services

• Student Life

• Faculty Positions

• Staff Positions

• Postdoctoral Positions

• Internships

• Employers Seeking Graduating Students

• Graduate Students Seeking Employment

• Official Press Releases

• Recent News

• Videos

• Events and Activities

• Talks and Seminars

The Optimal Distance Measure for Object Detection

Shyjan Mahamud and Martial Hebert
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2003.

Download

Adobe portable document format (pdf) (812KB) Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown to be the probability that a pair of input image measurements belong to different classes. In practice, we model the optimal distance measure using a linear logistic model that combines the discriminative powers of more elementary distance measures associated with a collection of simple to construct feature spaces like color, texture and local shape properties. Furthermore, in order to perform search over large training sets efficiently, the same framework was extended to find hamming distance measures associated with simple discriminators. By combining this discrete distance model with the continuous model, we obtain a hierarchical distance model that is both fast and accurate. Finally, the nearest neighbor search over object part classes was integrated into a whole object detection system and evaluated against an indoor detection task yielding good results.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center Associated Project(s): 2D Recognition

Text Reference

Shyjan Mahamud and Martial Hebert, "The Optimal Distance Measure for Object Detection," IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2003.

BibTeX Reference

@inproceedings{Mahamud_2003_4708,    author = "Shyjan Mahamud and Martial Hebert",    title = "The Optimal Distance Measure for Object Detection",    booktitle = "IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)",    year = "2003", }