Sub-Category Optimization through Cluster Performance Analysis for Multi-View Multi-Pose Object Detection-Reference-Cited by-同舟云学术

Sub-Category Optimization through Cluster Performance Analysis for Multi-View Multi-Pose Object Detection

Published:2011 Issue:7 Volume:E94-D Page:1467-1478
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

DAS Dipankar¹,KOBAYASHI Yoshinori¹,KUNO Yoshinori¹

Affiliation:

1. Department of Information and Computer Sciences, Saitama University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

http://www.jstage.jst.go.jp/article/transinf/E94.D/7/E94.D_7_1467/_pdf

Reference36 articles.

1. [1] P.A. Viola and M.J. Jones, “Rapid object detection using a boosted cascade of simple features,” CVPR, pp.511-518, Kauai, HI, USA, 2001.

2. [2] C. Huang, H. Ai, Y. Li, and S. Lao, “Vector boosting for rotation invariant multi-view face detection,” IEEE International Conference on Computer Vision, pp.446-453, IEEE Computer Society, Beijing, China, 2005.

3. [3] Z. Tu, “Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering,” IEEE International Conference on Computer Vision, pp.1589-1596, IEEE Computer Society, Beijing, China, 2005.

4. [4] T. Hofmann, “Unsupervised learning by probabilistic latent semantic analysis,” Mach. Learn., vol.42, no.1/2, pp.177-196, 2001.

5. [5] V. Ferrari, F. Jurie, and C. Schmid, “Accurate object detection with deformable shape models learnt from images,” CVPR, IEEE Computer Society, pp.1-8, Minneapolis, Minnesota, USA, 2007.