CSCI2951-B Data-Driven Vision and Graphics

Fall 2010, MWF 10:00 to 10:50, CIT 345.
Instructor: James Hays

Course Description

Course Catalog Entry
This graduate seminar course investigates current research topics in image-based graphics and vision. We will examine data sources, features, and algorithms useful for understanding and manipulating visual data. We will pay special attention to methods that harness large-scale or Internet-derived data. Vision topics such as scene understanding and object detection will be linked to graphics applications such as photo editing and image-based rendering. These topics will be pursued through independent reading, class discussion and presentations, and state-of-the-art projects.

The goal of this course is to give students the background and skills necessary to perform research in image-based graphics and vision. Students should understand the strengths and weaknesses of current approaches to research problems and identify interesting open questions and future research directions. Students will hopefully improve their critical reading and communication skills, as well.

Course Requirements

Reading and Summaries

Students will be expected to read one or two papers for each class. For every assigned paper, students must write a two or three sentence summary and identify at least one question or topic of interest for possible in class discussion. Interesting topics for discussion could relate to strengths and weaknesses of the paper, possible future directions, connections to other research, uncertainty about the conclusions of the experiments, etc. Reading summaries should be emailed to the instructor by 11:59pm the day before each class. Please put the course number, "2951", somewhere in the subject line. If you are presenting you don't need to turn in a summary.

Class participation

All students are expected to take part in class discussions. If you do not fully understand a paper that is OK. We can work through the unclear aspects of a paper together in class. If you are unable to attend a specific class please let me know ahead of time (and have a good excuse!).


Depending on enrollment, students will present one or two papers (or groups of papers) throughout the semester. Students are expected to implement some aspects of the presented material and perform experiments that help understand the algorithms. Presentations and all supplemental material should be ready one week before the presentation date so that students can meet with the instructor and go over the presentation and possibly iterate before the in-class presentation. For the presentations it is fine to use slides or code from outside sources (for example, the paper authors) but be sure to give credit.

Semester projects

Students are expected to complete a state-of-the-art research project on topics relevant to the course. Students will propose a research topic part way through the semester. After a project topic is finalized, students will meet occasionally with the instructor to discuss progress. The course will end with final project presentations. Students will also produce a conference-formatted write-up of their project. The ideal project is something with a clear enough direction to be completed in a couple of months, and enough novelty such that it could be published in a peer-reviewed venue with some refinement and extension.


Strong mathematical skills (linear algebra, calculus, probability and statistics) and previous imaging (graphics, vision, or computational photography) courses are needed. It is strongly recommended that students have taken one of the following courses (or equivalent courses at other institutions): If you aren't sure whether you have the background needed for the course, you can try reading some of the papers below or you can simply come to class during the shopping period.


We will not rely on a textbook, although the free, online textbook "Computer Vision: Algorithms and Applications" by Richard Szeliski is a helpful resource.


Your final grade will be made up from

Helpful Links:

Office Hours:

James Hays, Monday and Wednesday 11:00-12:00


DatePaperPaper, Project page, and MaterialPresenter
W, Sept 1 Introduction James
F, Sept 3 The state of vision and graphics James
Image representations, mid-level vision, and their applications
M, Sept 6No Classes
W, Sept 8 80 million tiny images: a large dataset for non-parametric object and scene recognition. A. Torralba, R. Fergus, W. T. Freeman. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30(11), 2008. pdf, project page James
F, Sept 10Using Contours to Detect and Localize Junctions in Natural Images. Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik. Computer Vision and Pattern Recognition (CVPR), 2008. pdf, project page James
M, Sept 13Object recognition from local scale-invariant features, David Lowe, ICCV 1999. pdf, project page James
optional reading Histograms of Oriented Gradients for Human Detection. Navneet Dalal and Bill Triggs. In Proceedings of IEEE Conference Computer Vision and Pattern Recognition, 2005. .pdf
optional reading Robust wide baseline stereo from maximally stable extremal regions. J. Matas, O. Chum, U. Martin, and T Pajdla. Proceedings of the British Machine Vision Conference, 2002. .pdf
optional reading Learning local image descriptors. Simon Winder and Matthew Brown. CVPR 2007. .pdf
W, Sept 15Video Google: A Text Retrieval Approach to Object Matching in Videos. Sivic, J. and Zisserman, A. Proceedings of the International Conference on Computer Vision (2003) pdf, project page James
optional reading Scalable Recognition with a Vocabulary Tree. David Nister and Henrik Stewenius. CVPR 2006 .pdf
optional reading Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases. Philbin, J. , Chum, O. , Isard, M. , Sivic, J. and Zisserman, A. CVPR 2008. .pdf
F, Sept 17 Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. S. Lazebnik, C. Schmid, and J. Ponce, CVPR 2006. pdf, project page David
M, Sept 20 Scene Completion Using Millions of Photographs. James Hays, Alexei A. Efros. ACM Transactions on Graphics (SIGGRAPH 2007). August 2007, vol. 26, No. 3. project page James
W, Sept 22 Matching Local Self-Similarities across Images and Videos. Eli Shechtman and Michal Irani. IEEE Conference on Computer Vision and Pattern Recognition 2007 (CVPR'07) project page Silvia
F, Sept 24 A Discriminatively Trained, Multiscale, Deformable Part Model. P. Felzenszwalb, D. McAllester, D. Ramanan. Computer Vision and Pattern Recognition (CVPR) 2008. pdf, project page Konstantin
M, Sept 27 An Empirical Study of Context in Object Detection. Santosh K. Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros, Martial Hebert. Computer Vision and Pattern Recognition (CVPR) 2009. project page James
optional reading Object Recognition by Scene Alignment. B. C. Russell, A. Torralba, C. Liu, R. Fergus, W. T. Freeman. Advances in Neural Information Processing Systems (NIPS), 2007. pdf
W, Sept 29 SUN Database: Large-scale Scene Recognition from Abbey to Zoo. J. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. IEEE Conference on Computer Vision and Pattern Recognition (CVPR2010). project page James
F, Oct 1 It's All About the Data. Tamara L. Berg, Alexander Sorokin, Gang Wang, David A. Forsyth, Derek Hoiem, Ali Farhadi, Ian Endres. Proceedings of the IEEE, Special Issue on Internet Vision, August 2010, 98-8, 1434-1453. .pdf by email James
M, Oct 4 Utility data annotation with Amazon Mechanical Turk. Alexander Sorokin, David Forsyth. In the First IEEE Workshop on Internet Vision at CVPR 08 pdf,project page Genevieve
W, Oct 6 ImageNet: A Large-Scale Hierarchical Image Database. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei. IEEE Computer Vision and Pattern Recognition (CVPR), 2009 pdf,project page Sirion
F, Oct 8 What does classifying more than 10,000 image categories tell us? J. Deng, A. Berg, K. Li and L. Fei-Fei. Proceedings of the 12th European Conference of Computer Vision (ECCV). 2010. pdf Konstantin
Image Geolocation
M, Oct 11No Classes
W, Oct 13IM2GPS: estimating geographic information from a single image. James Hays and Alexei Efros. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2008.project pageJames
optional reading Landmark classification in large-scale image collections. D. Crandall, Y. Li, and D. Huttenlocher. in ICCV 2009. pdf
F, Oct 15Image Sequence Geolocation with Human Travel Priors. Evangelos Kalogerakis, Olga Vesselova, James Hays, Alexei A. Efros, Aaron Hertzmann. Proceedings of the IEEE Internaltional Conference on Computer Vision Recognition (ICCV), 2009. project pageDonnie
Hallucinating Super-resolution
M, Oct 18 Example-based super-resolution. William T. Freeman, Thouis R. Jones, and Egon C. Pasztor. MERL Technical Report. pdf James
W, Oct 20 Context-Constrained Hallucination for Image Super-Resolution.J. Sun and M. F. Tappen. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2010). pdfTravis
F, Oct 22Image Upsampling via Texture Hallucination. Y. HaCohen, R. Fattal, D. Lischinski. IEEE International Conference on Computational Photography (ICCP 2010). project pageGeoff
LabelMe and Applications
M, Oct 25LabelMe: a Database and Web-based Tool for Image Annotation. B. C. Russell, A. Torralba, K. P. Murphy, W. T. Freeman. International Journal of Computer Vision, 2008. pdf, project page Sirion
W, Oct 27Building a database of 3D scenes from user annotations. B. C. Russell and A. Torralba. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. pdf, Project pageGeorge
F, Oct 29Photo Clip Art. Jean-François Lalonde, Derek Hoeim, Alexei A. Efros, Carsten Rother, John Winn and Antonio Criminisi. ACM Transactions on Graphics (SIGGRAPH 2007). project page Yun
Applications of Unlabeled Scene Matching
M, Nov 1Creating and exploring a large photorealistic virtual space. J. Sivic, B. Kaneva, A. Torralba, S. Avidan and W. T. Freeman. First IEEE Workshop on Internet Vision, associated with CVPR 2008.pdfSeth
W, Nov 3Image restoration using online photo collections. K. Dale, M.K. Johnson, K. Sunkavalli, W. Matusik and H. Pfister. International Conference on Computer Vision, 2009.project pageYun
F, Nov 5CG2REAL. M.K. Johnson, K. Dale, S. Avidan, H. Pfister, W.T. Freeman and W. Matusik.. IEEE Trans. on Visualization and Computer Graphics, to appear 2010.project pageDonnie
M, Nov 8 Segmenting Scenes by Matching Image Composites. B. C. Russell, A. A. Efros, J. Sivic, W. T. Freeman, and A. Zisserman. NIPS 2009 pdf David
Saliency and Image Synthesis
W, Nov 10Learning to predict where humans look. T. Judd, K. Ehinger, F. Durand, and A. Torralba. IEEE International Conference on Computer Vision (ICCV), 2009.project pageTravis
optional readingPhotoSketch: A sketch based image query and compositing system. Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur, and Marc Alexa. ACM SIGGRAPH 2009 - Talk Program.project page
F, Nov 12Sketch2Photo: Internet Image Montage. ACM SIGGRAPH ASIA 2009, ACM Transactions on Graphics. Tao Chen, Ming-Ming Cheng, Ping Tan, Ariel Shamir, Shi-Min Hu.project pageMichael
Social Vision
M, Nov 15Autotagging Facebook: Social Network Context Improves Photo Annotation. Stone, Z.; Zickler, T.; Darrell, T. First IEEE Workshop on Internet Vision, (2008).project pageGenevieve
W, Nov 17Estimating Age, Gender and Identity using First Name Priors. A. Gallagher, T. Chen. IEEE Conference on Computer Vision and Pattern Recognition 2008.project pageSeth
F, Nov 19Understanding Images of Groups of People. A. Gallagher, T. Chen. IEEE Conference on Computer Vision and Pattern Recognition 2009.project pageJames
M, Nov 22Describing Objects by Their Attributes. A. Farhadi, I. Endres, D. Hoiem, and D.A. Forsyth. CVPR 2009 project pageJames
W, Nov 24No Classes
F, Nov 26No Classes
M, Nov 29Class canceled
Photo Tourism
W, Dec 1Photo tourism: Exploring photo collections in 3D. Noah Snavely, Steven M. Seitz, Richard Szeliski. ACM Transactions on Graphics (SIGGRAPH Proceedings), 25(3), 2006.pdf, project pageGeorge
F, Dec 3Scene Summarization for Online Image Collections. Ian Simon, Noah Snavely, and Steven M. Seitz. In ICCV, 2007. pdfGeoff
M, Dec 6LabelMe video: Building a Video Database with Human Annotations. J. Yuen, B. C. Russell, C. Liu, and A. Torralba. IEEE International Conference on Computer Vision (ICCV), 2009.pdf, project pageSilvia
W, Dec 8A data-driven approach for event prediction. Jenny Yuen, Antonio Torralba. European Conference on Computer Vision (ECCV), 2010. pdfMichael
M, Dec 13, 2pmFinal Project Presentations Everybody


Ideas for the organization and content of this course came from many other researchers such as Svetlana Lazebnik, Kristin Grauman, Antonio Torralba, Derek Hoeim, and Alexei Efros.

Related Graduate Seminars at other Universities (although these have more of a vision and learning emphasis)