School of Computing

Korea Advanced Institute of Science and Technology (KAIST)

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CS688: Web-Scale Image Retrieval (Fall 2018)

Instructor: Sung-eui Yoon

We took a photo together in our final lecture.
We took a picture in our last lecture.



Course overview

We extract feature points between two similar images and match them, followed by overlaying them together based on the matched points in the bottom row.

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:

What you will do:


Lecture schedule (subject to change)

Date Topics and slides Related material(s)
  • Aug. 28 (T)
Overview on the course and course policy
  • Aug. 30 (Th)
Keypoint Localization
  • Sep. 4 (T)
  • Sep. 6 (Th)
No class due to attending BMVC
  • Sep. 11 (T)
Scale Invariant Region Selection and SIFT
  • Sep. 13 (Th)
Linear Classification
  • Sep. 18 (T)
Bag-of-Words (BoW) Models for Local Descriptors
  • Sep. 27 (Th)
Deep Neural Nets and Features
  • Oct. 2 (T)
Image Search with Deep Learning
  • Oct. 4 (Th)
Hashing Techniques
  • Oct. 23 (T)
  • Oct. 25 (Th)
  • Oct. 30 (T)
  • Nov. 1 (Th)
  • Nov. 6 (T)
  • Nov. 8 (Th)
No class due to attending a KAIST committee meeting
  • Nov. 13 (T)
  • Nov. 15 (Th)
  • Nov. 20 (T)
  • Nov. 22 (Th)
  • Nov. 27 (T)
  • Nov. 29 (Th)
No class due to undergraduate interview
  • Dec. 4 (T)
  • Dec. 6 (Th)
Final project presentation
  • Dec. 11 (T)
  • Dec. 13 (Th)
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

Computer vision resources (papers, videos, code, datasets, etc.):

Paper search:


Acknowledgements: The course materials are based on those of Prof. Fei-Fei Li, Stanford. Thank you so much!


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