Instructor:	Jason Corso (jcorso)
Course Webpage:	http://www.cse.buffalo.edu/~jcorso/t/CSE702
Meeting Times:	T 9-11:30
Location:	Lockwood B20A
Office Hours:	Monday 1-3 or by appointment.

News

• For the first week, we will meet briefly to discuss the paper list.

Main Course Material

Course Overview: This course will explore the topic of semantics in image and video analysis. We will read and discuss papers on this topic throughout the semester, with the students primarily in charge of leading the discussions.

Prerequisites: It is assumed that the students have significance experience with computer vision, machine learning, and image analysis.

Grading: Grading is P/F unless a student specifically request otherwise.

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Course Outline

See the paper list below for the full paper citations. I just list the authors here. Paper List
PDFs of all papers are available in ~jcorso/702 on the CSE (not the VPML) network.

• R. K. Srihari and D. T. Burhans. Visual semantics: Extracting visual information from text accompanying pictures. In Proceedings of AAAI-94, 1994.
• J. R. Bender. Connecting language and vision using a conceptual semantics. Master's thesis, Massachusetts Institute of Technology, 2001.
• I. Biederman. On the Semantics of a Glance at a Scene. In M. Kubovy and K. R. Pomerantz, editors, Perceptual Organization, pages 213-263. Lawrence Erlbaum Publisher, 1981.
• I. Biederman. Recognition-by-Components: A Theory of Human Image Understanding. Pschological Review. 1987.
• M. Boutell and J. Luo. A Generalized Temporal Context Model for Semantic Scene Classification. In IEEE Conference on Computer Vision and Pattern Recognition, 2004.
• B. Bradshaw, B. Scholkopf, and J. C. Platt. Kernel Methods for Extracting Local Image Semantics. Technical Report 99, Microsoft Research, 2001.
• J. Fan, Y. Gao, H. Luo, and R. Jain. Mining Multilevel Image Semantics via Hierarchical Classification. IEEE Transactions on Multimedia, Vol. 10, No. 2. pp. 167-187. 2008.
• H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In Proceedings of the International Conference on Machine Learning, 2009.
• J. Luo, A. E. Savakis, and A. Singhal. A bayesian network-based framework for semantic image understanding. Pattern Recognition, 38(6):919-934, 2005.
• C. Meini and A. Paternoster. Understanding language through vision. Artificial Intelligence Review, 10(1-2):37-48, 1996.
• M. R. Naphade and T. S. Huang. A Probabilistic Framework for Semantic Video Indexing, Filtering, and Retrieval. IEEE Transactions on Multimedia, 3(1):141-151, 2001.
• J. Z. Wang, J. Li, and G. Wiederhold. SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947-963, 2001.
• S. C. Zhu and D. Mumford. A stochastic grammar of images. Foundations and Trends in Computer Graphics and Vision, 2(4):259-362, 2007.
• G. Carneiro, A.B. Chan, P.J. Moreno, and N. Vasconcelos. Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):394-410, 2007.
• Y. Liu, D. Zhang, G. Lu, and W.Y. Ma. A survey of content-based image retrieval with high-level semantics. Pattern Recognition, 40(1):262-282, 2007.
• V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. In Proceedings of Advance in Neutral Information Processing, 2003.
• R. Zhao and W. I. Grosky. Bridging the semantic gap in image retrieval. Distributed multimedia databases: Techniques and applications, pages 14-36, 2001.
• K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In International Conference on Computer Vision, volume 2, pages 408-415, 2001.
• Y. Lu, C. Hu, X. Zhu, H. J. Zhang, and Q. Yang. A unified framework for semantics and feature based relevance feedback in image retrieval systems. In Proceedings of the eighth ACM international conference on Multimedia, pages 31-37, 2000.