Visual Recognition and Search

EECS 6890 Topics in Information Processing (3 Credits)

Spring 2013, Columbia University | Thursdays 7:00-8:50pm, 327 Seeley W. Mudd

Instructors: Rogerio Feris and Liangliang Cao {rsferis, liangliang.cao}

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Final Project Presentations:

Macro-Scale Vision: Morphology Classification in Galaxy Imagery [Brendan Jou, Joseph G. Ellis, and Jie Feng]

Large-Scale Galaxy Image Retrieval [Yin Cui, Yongzhou Xiang, and Kun Rong]

Fusing Feature Descriptors for Action Recognition in Videos [Qian Liang, Chen Xue, and Jiawei Chen]

Historical Building Finder (Columbia Tour VIDEO DEMO) [Yanling Zhang, Yaqing Mao, Yan Peng]

RGBD Face Detection [ZhongJie Bi]

General Info

Projects will be done in groups of two or three students depending on the total number of students in class. We expect students to spend significant time on their projects. Each team will have to write a paper (4-8 pages) as a final technical report.


Keep checking important dates for deadlines regarding projects.

Project Ideas and Proposal

We have prepared a set of project ideas for you. Each team will have to either choose one of these ideas or come up with their own project proposal, which should be related to the syllabus and approved by the instructors. You may check the resources link for publicly available source code and datasets, which can serve as basis for projects. Students will have to prepare a project proposal presentation describing their project plan.

Project Updates

As part of the mid-project evaluation, each group of students will have to give two short update presentations (see Project Update 1 and Project Update 2) in class describing their progress.

Final Project Report

Each group of students will have to write a project paper (4-8 pages) and give a presentation in class summarizing the main project goals and accomplishments.

Project Grading

Projects account for 60% of your final grade, and will be graded according to the following criteria:

  • Project Updates (15%)
  • Final Project Report (40%)
    • Write-up (15%) - Clarity, language, organization, literature survey, references, discussion
    • Technical (15%) - Originality, correctness, depth
    • Evaluation and Results (10%) - Thoroughness in analysis and experimentation, results and performance
  • Project Presentation (5%)