CS688: Large-Scale Image & Video Retrieval (Spring 2020)
Instructor: Sung-eui Yoon
- When: 4:00-5:30 pm on Tue. and Thu.
- Where: Lecture room 113, Kim Beang-Ho & Kim Sam-Youl ITC Building (N1) (map)
- First class: March 17
- Textbook: In-class handouts and ongoing draft (pdf), ongoing draft (web) on image search
- Board: KLMS
- Question Page: Question Submission
- Paper Submission Page: Paper Summary Submission (before every Tue. class)
- Course video page: youtube list
Outline
- Course overview
- Lectures and tentative schedule
- Student presentations
- Additional reference materials
Course overview
Thanks to rapid advances of digital camera and various image processing tools, we can easily create new pictures, images, and videos for various purposes. This in turn results in a huge amount of images in the internet and even in personal computers. For example, flickr, an image hosting website, contains more than five billion images and flickr members update more than three thousands image every minute.
These huge image databases pose numerous technical challenges in terms of image processing, searching, storing, etc. In this class we will discuss various scalable techniques for web-scale image/video databases and novel applications that can utilize such data.
In summary, what you will get at the end of the course:
- Broad understanding on image/video retrieval techniques
- In-depth knowledge on recent methods that can handle web-scale data
- Study novel applications that utilize web-data
What you will do:
- Choose and present a few papers from recent conferences.
- Final project: come up with your own idea related to the topic, (optionally) implement it to improve the state-of-the-art techniques
- Mid-term exam: reviewing basic image retrieval methods
Lecture schedule (subject to change)
| Date | Topics and slides | Related material(s) |
|---|---|---|
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Overview on the course and course policy | |
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Classical keypoint localization | |
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Scale Invariant Region Selection and SIFT | |
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Deep Neural Nets and Features | |
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Convolutional Neural Networks | |
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Bag-of-Words (BoW) Models for Local Descriptors | |
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Inverted Index | |
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Hashing Techniques | |
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Programming Assignment 2 | |
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CNN based Image Search | |
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Mid-term exam(E3-1 3444, 4:00PM) | |
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No class due to Buddha's birthday | |
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No class due to children's day | |
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Students Presentation I: 1. XU YIN |
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Students Presentation I: 1. GUOYUAN AN 2. Junsik Jung |
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Students Presentation I: 1. Changho Jo 2. Chongsoo Chang |
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Mid-term presentation: 1. GUOYUAN AN |
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Mid-term presentation: 1. Changho Jo 2. Chongsoo Chang 3. Junsik Jung |
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Mid-term presentation: 1. XU YIN |
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Students Presentation II: 1. Junsik Jung 2. Changho Jo |
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Students Presentation II: 1. XU YIN 2. Chongsoo Chang |
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Students Presentation II: 1. GUOYUAN AN |
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No class (CVPR 20) | |
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No class (CVPR 20) | |
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Final presentation: 1. XU YIN 2. GUOYUAN AN |
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Final presentation: 1. Changho Jo 2. Chongsoo Chang 3. Junsik Jung |
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Reserved (final exam) |
Student presentations and reports
For your presentations, please use the this powerpoint template; paper presentation
guideline is available.
For your final report, please use the this latex template
Additional reference materials and links
- WST665/CS770 homepage at fall of 2011
- WST665/CS688 homepage at fall of 2012
- WST665/CS688 homepage at fall of 2014
- CS688 homepage at fall of 2016
- CS688 homepage at fall of 2018
Computer vision resources (papers, videos, code, datasets, etc.):
- CVPapers, Vision talk videos
- Video lectures:
- Multimedia Information Retrieval
- VLFeat: contains popular computer vision algorithms including SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, and quick shift
Paper search:
Acknowledgements: The course materials are based on those of Prof. Fei-Fei Li, Stanford. Thank you so much!