Incorporating camera metadata for attended region detection and consumer photo classification

MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Volumn , Issue , 2009, Pages 517-520

  Li, Zhong ^a   Luo, Hangzai ^b   Fan, Jianping ^a  

^a University of North Carolina at Charlotte   (United States)

^b EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Attended regions; Camera metadata; Image classification

Indexed keywords

ATTENDED REGIONS; AUTOMATIC UNDERSTANDING; CAMERA METADATA; HUMAN BEING; INTERESTINGNESS; PARAMETERS SETTING; PHOTO CLASSIFICATION; REGION DETECTION; SURVEILLANCE CAMERAS; TAKE-ALL; VISION SENSORS;

CAMERAS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RETRIEVAL; METADATA; METHOD OF MOMENTS; MULTIMEDIA SYSTEMS; TECHNICAL PRESENTATIONS;

SECURITY SYSTEMS;

EID: 72449177891     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1631272.1631345     Document Type: Conference Paper

References (8)

1. A. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain, Content-based image retrieval at the end of the early years, PAMI, vol.22, pp.1349-1380, 2000.
2. R. Desimone, J. Duncan,"Neural mechanisms of selective visual attention, annual reviews", Neuroscience, vol.18, pp.193-222, 1995.
3. C. M. Privitera, L. W. Stark, "Algorithms for defining visual region-of-interest: Comparison with eye fixations", PAMI, vol.22, pp.970-982, 2000.
4. C. Siagian, L. Itti, "Rapid biologically-inspired scene classification using features shared with visual attention", PAMI, vol.29, pp.300-312, 2007.
5. Y. Ma, H. Zhang, "Contrast-based image attention analysis by using fuzzy growing", ACM Multimedia, pp.374-381, 2003.
6. J. Luo, A.E. Savakis, A. Singhal, "A Bayesian network-based framework for semantic image understanding", Pattern Recognition, vol.38, pp.919-934, 2005.
7. M. Boutell, J. Luo, "Bayesian fusion of camera metadata cues in semantic scene classification", IEEE CVPR, pp.623-630, 2004.
8. J. Fan, Y. Gao, H. Luo, "Integrating concept ontology and multi-task learning to achieve more effective classifier training for multi-level image annotation", IEEE Trans. on Image Processing, vol. 17, no.3, pp.407-426, 2008.