CS688: Web-Scale Image Retrieval (Fall 2018)
Instructor: Sung-eui Yoon
- When: 2:30-4:00 pm on Tue. and Thu.
- Where: Lecture room 406, Creative Learning Building (E11) (map)
- First class: August 28
- 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)
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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Keypoint Localization | |
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No class due to attending BMVC | |
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Scale Invariant Region Selection and SIFT | |
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Linear Classification | |
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Bag-of-Words (BoW) Models for Local Descriptors | |
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Deep Neural Nets and Features | |
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Image Search with Deep Learning |
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Hashing Techniques |
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No class due to attending a KAIST committee meeting | |
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No class due to undergraduate interview | |
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Final project presentation | |
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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
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!