A combination of generative and discriminative approaches to object detection

Junyeong Yang, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents a new simple algorithm which combines generative and discriminative approaches to object detection. The research makes two key contributions. The first contribution is the introduction of a new algorithm called the DT (Decomposition-Tree) which is capable of clustering on the manifold of object patterns(using Gaussian clusters) and determining the thresholds of each cluster by using hard samples which are selected during learning. The second contribution is that the learning time of the DT algorithm has been reduced rapidly. Because the DT algorithm shows spatial relationships of training patterns in the form of a tree, it requires relearning rather than new learning. To evaluate the performance of the proposed object detection algorithm, we experimented with face detection. The DT algorithm yields face detection performance comparable to that of the best previous systems [4]

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages249-253
Number of pages5
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 2006 Aug 202006 Aug 24

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
Country/TerritoryChina
CityHong Kong
Period06/8/2006/8/24

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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