Object detection with geometric context of keypoints described as lifetime

Changxin Gao; Jun Gao; Qiling Tang; Nong Sang

doi:10.1117/12.832536

30 October 2009 Object detection with geometric context of keypoints described as lifetime

Changxin Gao, Jun Gao, Qiling Tang, Nong Sang

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749606 (2009) https://doi.org/10.1117/12.832536
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Changxin Gao, Jun Gao, Qiling Tang, and Nong Sang "Object detection with geometric context of keypoints described as lifetime", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749606 (30 October 2009); https://doi.org/10.1117/12.832536

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