Scene modeling in global-local view for scene classification

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Scene modeling in global-local view for scene classification

Full text

Pdf (2.89 MB)

Source

Conference On Image And Video Retrieval archive
Proceedings of the 2008 international conference on Content-based image and video retrieval table of contents

Niagara Falls, Canada

POSTER SESSION: Poster/reception table of contents

Pages 179-184

Year of Publication: 2008

ISBN:978-1-60558-070-8

Authors

Aiwen Jiang	Chinese Academy of Sciences, Beijing, China
Chunheng Wang	Chinese Academy of Sciences, Beijing, China
Baihua Xiao	Chinese Academy of Sciences, Beijing, China

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 12, Downloads (12 Months): 110, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1386352.1386380 What is a DOI?

ABSTRACT

Scene classification aims to automatically label an image among a set of semantic categories. The issue of scene modeling is critical to its classification performance. Inspired by recent psychology progresses on visual perception, we unify the current popular strategies into a 'gist' framework, and suggest a global-local view to model scenes. We evaluate our strategy on the 13 class scenes dataset mostly cited. The experiment results show that our method significantly outperforms the state-of-art methods. We believe it will give a fresh look at how to effectively model scene to benefit for scene analysis.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Aude Oliva , Antonio Torralba, Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope, International Journal of Computer Vision, v.42 n.3, p.145-175, May-June 2001 [doi>10.1023/A:1011139631724]
	2	Oliva, A. Gist of the Scene. Neurobiology of Attention. 251--256, 2005.
	3	Oliva, A. and Torralba, A. Building the gist of a scene: the role of global image features in recognition. Progress in Brain Research, 155. 23--36, 2006.
	4	Quelhas, P., Monay, F., Odobez, J.--M., Gatica--Perez, D. and Tuytelaars, T. A Thousand Words in a Scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (9). 1575--1589, 2007.
	5	Anna Bosch , Xavier Muñoz , Robert Martí, Review: Which is the best way to organize/classify images by content?, Image and Vision Computing, v.25 n.6, p.778-791, June, 2007 [doi>10.1016/j.imavis.2006.07.015]
	6	Bosch, A., Zisserman, A. and Munoz, X. Scene Classification via pLSA European Conference on Computer Vision, 2006.
	7	Fei-Fei Li , Pietro Perona, A Bayesian Hierarchical Model for Learning Natural Scene Categories, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2, p.524-531, June 20-26, 2005 [doi>10.1109/CVPR.2005.16]
	8	Vailiya, A., Figueiredo, A. and Jain, A.K. Image classification for content-based indexing. IEEE Transactions on Image Processing, 10 (1). 117--130, 2001.
	9	Chang, E., Goh, K., Sychay, G. and Wu, G. CBSA: Content-based soft annotation for multimodel image retrieval using bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology, 13 (1). 26--38, 2003.
	10	Serrano, N., Savakis, A.E. and Luo, J. Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition, 37 (9). 1773--1784, 2004.
	11	Thomas Hofmann, Unsupervised learning by probabilistic latent semantic analysis, Machine Learning, v.42 n.1/2, p.177-196, Jan. 2001 [doi>10.1023/A:1007617005950]
	12	Thorsten Brants, Test Data Likelihood for PLSA Models, Information Retrieval, v.8 n.2, p.181-196, April 2005 [doi>10.1007/s10791-005-5658-8]
	13	David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, v.60 n.2, p.91-110, November 2004 [doi>10.1023/B:VISI.0000029664.99615.94]
	14	Josef Sivic , Andrew Zisserman, Video Google: A Text Retrieval Approach to Object Matching in Videos, Proceedings of the Ninth IEEE International Conference on Computer Vision, p.1470, October 13-16, 2003
	15	Fergus, R., Perona, P. and Zisserman, A. Object class recognition by unsupervised scale-invariant learning IEEE International Conference on Computer Vision and Pattern Recogntion, 2003.
	16	Julia Vogel , Bernt Schiele, Semantic Modeling of Natural Scenes for Content-Based Image Retrieval, International Journal of Computer Vision, v.72 n.2, p.133-157, April 2007 [doi>10.1007/s11263-006-8614-1]
	17	K. Mikolajczyk , T. Tuytelaars , C. Schmid , A. Zisserman , J. Matas , F. Schaffalitzky , T. Kadir , L. Van Gool, A Comparison of Affine Region Detectors, International Journal of Computer Vision, v.65 n.1-2, p.43-72, November 2005 [doi>10.1007/s11263-005-3848-x]
	18	Krystian Mikolajczyk , Cordelia Schmid, A Performance Evaluation of Local Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.27 n.10, p.1615-1630, October 2005 [doi>10.1109/TPAMI.2005.188]