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ITCS 6157

ITCS 6157. Visual Database Fall 2014. http://www.cs.uncc.edu/~jfan/itcs6157.html. Overview. Class hour 9:30AM - 12:15PM, Friday Office hour Friday 12:30 - 5:00PM Classroom Woodward Hall 140 Instuctor - Dr. Jianping Fan email - jfan@uncc.edy Office – Woodward 205D Webpage

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ITCS 6157

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  1. ITCS 6157 Visual Database Fall 2014 http://www.cs.uncc.edu/~jfan/itcs6157.html

  2. Overview • Class hour 9:30AM - 12:15PM, Friday • Office hour Friday 12:30 - 5:00PM • Classroom Woodward Hall 140 • Instuctor - Dr. Jianping Fan • email - jfan@uncc.edy • Office – Woodward 205D • Webpage http://www.cs.uncc.edu/~jfan • Textbook: we will use the slices and papers on the course web page

  3. Why we should have this course? • Internet is changing the world • Multimedia is dominating the content of Internet • Easy access of multimedia content through Internet could be the future of IT This class will provide training on multimedia content analysis and search!

  4. Why we should have this course? • Good job market: Google, Yahoo!.... • Have fun: solving real problem • Not so “hard” to learn (??) • Next generation search engines

  5. Course objectives Google, Yahoo! & MSN IE How can I access multimedia in database over networks? Networks User Multimedia Server

  6. Course objectives To answer above question, we need to address: 1. How to format multimedia queries? 2. How to represent multimedia content? 3. How to index large-scale multimedia? 4. How to search multimedia in database ? 5. How to transmit query results over IP ? 6. How to control user’s access ?

  7. Can we do multimedia retrieval like Google for text search? How to build multimedia search engines? Yahoo, Google How to build text indexing? Natural language processing Keywords Text document Text database Inverse File indexing Simple extension multimedia analysis Multimedia data Multimedia ``keywords” Multimedia database & query Hash Indexing or others

  8. Required Techniques How to build multimedia search engines? • Computer Vision Technologies for Multimedia Content Analysis • Machine Learning Tools for Understanding Multimedia Semantics • Database Techniques for Large-Scale Media Indexing • Human-Computer Interaction for query formulation, display & exploration

  9. Components from Database System • Data Representation Schema • Database Indexing • Database Storage • Query Management • Big Data Analytics

  10. Components from Computer Vision • Image & Video Analysis & Feature Extraction • Object Detection & Scene Understanding • Classifier Training for object and concept detection • Scene Configuration and Structure

  11. Components from Machine Learning • GMM & Bayesian Network • Support Vector Machine (SVM) • Graphical Models & Structure Learning • Statistical Inference • Big Data Analytics

  12. Pre-Requirements of this Class • Database Management System: ITCS6160 or ITCS3160 • Computer Vision • Machine Learning • Programming Skills • Willing to work hard If you do not have these background, you should

  13. Course Topics • Data Clustering Tools • Machine Learning Techniques • Multimedia Analysis Technologies • Database Indexing Structures • Big Data Analytics and Exploration • Human-Computer Interaction Tools • Taking-Home Self-Study Materials • Open Discussion & Student Presentation

  14. Grading • Composition • Project 25% • Midterm 35% • Final 40% • Scale • >93% = A • 75-93% = B • 55-74% = C • <55% or cheating = F

  15. Class Policy • You have to attend the class & come to classroom on time (no later than 9:35am) • You should be ready to learn from the class: project implementation is critical • You should respect your classmates: come to learn from their presentation!

  16. Classroom Policy • No food!!! Drink can be allowed. • Small talk is not allowed, but you are welcome to ask question! • Walking inside classroom is not allowed within presentation time!

  17. Course Projects We will have two projects: • Project implementation project: you need to set up a team or individual to implement one small system for multimedia content analysis or understanding. • Paper presentation project: you need to pick one topic to present in the class. • More information • http://www.cs.uncc.edu/~jfan

  18. Implementation Project • Develop image/video analysis system using Visual C++ and Java. • Each group consists 3-4 students • 3-4 hours workload each week is expected • Java or C++ assumed • Talk to your professor to decide which algorithm you may implement for your project, discuss progress with your professor if necessary • Demonstrate your implementation to your professor & get feedback

  19. Paper Presentation Project • Present one research topic: you need to talk to your professor to get relevant research papers, prepare presentation slides & present in the class. • Well-understanding of the topic • Good presentation in the class • Be able to answer questions from classmates & professor • Topic selection: depending on available topics and professor assignment.

  20. Course Projects If you do wonderful job on course project, you may expect: • Good grade even you may perform well in final and mid-term tests • Practical implementation means more than paper work • Good recommendation letter for job hunting: professor can only memorize students with good performance! • Research position opportunities

  21. Midterm & Final • closed books and notes • One page notes is permitted • Cumulative • No makeup • Bonus is expected • Key components for your final grade

  22. Suggestions from Instructor • Do your best in the class • Show your problems to the instructor when you cannot make it • Show the evidence to us if you think you are right. • Open discussion is welcome, but no small talk

  23. Google Search Engine Who cares? • Google Search Engine

  24. Who cares?

  25. The way to join them • Good grade from class • More training on programming skills, especially for multimedia analysis, indexing and retrieval • Get recommendation from professor

  26. Recommendation • Good grade is very important, but it is not everything! • Learning something and solving one problem you like may be more important! • Learning from someone who may make you better! Especially your classmates

  27. What areas we will touch? • Computer Vision • Database • Information Retrieval • Machine Learning & AI • Visualization • Networks • Statistics & Security

  28. What you may expect • Start-up Companies Product search engine for amezon.com, taobao.com

  29. What you may expect • Start-up Companies Google Glass App: Google glass may change world like i-phone

  30. What you may expect • Start-up Companies Digital Camera App: Sony may sale digital cameras with your media organization & search software.

  31. What you may expect • Start-up Companies Personal Computer App: IBM Dell may sale PCs with your media organization & search software.

  32. What you may expect • Start-up Companies Automatic-Driving Car App: BMW Tesla may sale cars with your object recognition & navigation systems.

  33. What you may expect • Start-up Companies Multimedia Search Engine: Google will definitely care! Do not forget to come back to support our class!

  34. why not ask "stupid" questions? Do your best & have fun! Good students should be able to push your professor to think and work harder not easier!

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