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Proceedings Paper

Object detection with geometric context of keypoints described as lifetime
Author(s): Changxin Gao; Jun Gao; Qiling Tang; Nong Sang
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Paper Abstract

We develop a novel approach for object detection and location task. This paper proposed a novel method to represent local regions around keypoints, called lifetime. Lifetime of a keypoint is used to describe its stability. Together with geometric relationships extractor, lifetime representations are embedded into a bag-of-features framework. The framework has following properties. First, the keypoints are represented as the lifetime rather than vector-quantized. Second, a simple and computationally efficient spatial pyramid structure is used to extract the geometric relationships between the keypoints. We demonstrate the efficacy of the proposed approach on UIUC car dataset. The experimental results showed that our approach has an excellent performance for object detection and localization.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749606 (30 October 2009); doi: 10.1117/12.832536
Show Author Affiliations
Changxin Gao, Huazhong Univ. of Science and Technology (China)
Jun Gao, Huazhong Univ. of Science and Technology (China)
Qiling Tang, Huazhong Univ. of Science and Technology (China)
Nong Sang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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