Department of Computer Science
Div. of Web Science and Technology
Korea Advanced Institute of Science and Technology (KAIST)

CS688/WST665: Web-Scale Image Retrieval (Fall 2014)

CS688/WST665: Web-Scale Image Retrieval (Fall 2014)

Instructor: Sung-eui Yoon

When and where: 4:00-5:15pm on Tue. and Thur. at Room 3445 in the CS building
First class: Sep-2 (Important announce for the first class)
Textbook: In-class handouts and ongoing draft on image search
Board: Noah board
Previous Board(Fall 2012): Noah board(2012)
Question Page: Question Submission
Paper Submission Page: Paper Summary Submission



  • Course overview
  • Lectures and tentative schedule
  • Student presentations
  • Additional reference materials
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    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:

  • 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 a paper list
  • 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
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    Lecture schedule (subject to change)

    # of lecture, date Topics and slides Related material(s)
    Sep-2 (T)
    Overview on the course and course policy Important announcement on the first class
    Sep-4 (Th)
    Sep-11 (Th)
    Keypoint localization
    Scale Invariant Region Selection
    Code of Harris detector
    Code of Blob Laplacian
    Sep-16 (T)
    Sep-18 (Th)
    Intro to Object Recognition
    Programming Assignment1
    Sep-23 (T)
    Sep-25 (Th)
    Bag-of-Words(BoW) Models Programming Assignment2
    PA2 dataset
    Student Presentation Guidance
    Sep-30 (T)
    Oct-2 (Th)
    Recent Image Retrieval Techniques
    Oct-7 (T)
    Invited Talk on Geometric Computing
    Oct-14 (T)
    Oct-16 (Th)
    Hashing Techniques
    Web-Scale Image Databases and Their Applications
    Project Guidelines
    Oct-21 (T)
    Oct-23 (Th)
    Oct-28 (T) 박희웅 오은수 윤웅직
    Oct-30 (Th) 김병주 김인엽 이형석
    Nov-4 (T) 김태영 김동신 나원준 이윤석
    Nov-6 (Th) 김태연 양현철 이예강
    Nov-11 (T) 권용선 박진선 이가영
    Nov-13 (Th) 김수지 유병인 최창민
    Nov-18 (T) Mid-project presentation:
    Nov-20 (Th) Mid-project presentation:
    Nov-25 (T) 윤웅직 오은수 권용선
    Nov-27 (Th) 이윤석 김수지 김동신
    Dec-2 (T) 이예강 이가영 김병주 이형석
    Dec-4 (Th) 김인엽 유병인 김태영
    Dec-9 (T) 박진선 나원준 양현철
    Dec-11 (Th)
    최창민 김태연 박희웅
    Dec-16 (T)
    Final-project presentation:
    Dec-18 (Th)

    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

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

  • CVPapers
  • 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:

  • Google scholar
  • Tim Rowley's graphics paper collections
  • Ke-Sen Huang's graphics paper collections
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    Acknowledgements: The course materials are based on those of Prof. Fei-Fei Li, Stanford. Thank you so much! Line

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