Transcript Start-up Companies - Department of Software and Information

ITCS 6157/8157
Visual Database
Fall 2015
http://www.cs.uncc.edu/~jfan/itcs6157.html
Overview
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Class hour 9:30AM - 12:15PM, Thursday
Office hour Thursday 1:30 - 5:00PM
Classroom Woodward Hall 130
Instructor - Dr. Jianping Fan
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email - [email protected]
Office – Woodward 205D
Webpage
http://www.cs.uncc.edu/~jfan
Textbook: we will use the slices and papers on the
course web page
Why we should have this course?
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Internet is changing the world
Multimedia (especially visual data) 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!
Why we should have this course?
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Good job market: Google, Yahoo!....
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Have fun: solving real problem
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Not so “hard” to learn (??)
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Next generation search engines
Course objectives
Google, Yahoo! & MSN IE
How can I access multimedia
in database over networks?
Networks
User
Multimedia Server
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 ?
Everyone has smartphone now
Can we do multimedia retrieval like
Google for text search?
How to build multimedia search engines?
How to build text indexing? Yahoo, Google
Natural language processing
Text document
Text database
Keywords
Inverse File indexing
Simple extension
Multimedia data
multimedia analysis
Multimedia database & query
Multimedia ``keywords”
Hash Indexing or others
Required Techniques
How to build multimedia search engines?
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Computer Vision Technologies for
Multimedia Content Analysis
Machine Learning Tools for Understanding
Multimedia Semantics
Database Techniques for Large-Scale
Multimedia Indexing
Human-Computer Interaction for query
formulation, display & exploration
Components from Database System
a. Data Representation Schema
b. Database Indexing
c. Database Storage
d. Query Management
e. Big Data Analytics
Components from Computer Vision
a. Image & Video Analysis & Feature Extraction
b. Object Detection & Scene Understanding
c. Classifier Training for object and concept detection
d. Scene Configuration and Structure
Components from Machine Learning
a. GMM & Bayesian Network
b. Support Vector Machine (SVM)
c. Graphical Models & Structure Learning
d. Statistical Inference
e. Deep Learning & Big Data Analytics
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Database Management System: ITCS6160
or ITCS3160
Computer Vision
Machine Learning
Programming Skills
If you do not have these background, you should
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Willing to work hard
Course Topics
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Data Clustering Tools
Machine Learning Techniques
Multimedia Analysis Technologies
Database Indexing Structures
Deep Learning & Big Data Analytics
Human-Computer Interaction Tools
Taking-Home Self-Study Materials
Open Discussion & Student Presentation
Grading
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Composition
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Project
Midterm
Final
25%
35%
40%
Scale
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>93% = A
75-93% = B
55-74% = C
<55% or cheating = F
If you miss 3 classes (three weeks) or more, you are
not allowed to take tests (mid-term and final)!
Class Policy
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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 could be
critical
You should respect your classmates: come
to learn from their presentation!
Classroom Policy
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No food!!! Drink could be allowed & Cell
Phone should be turned off.
Small talk is not allowed, but you are
welcome to ask questions!
Walking inside classroom is not allowed
within presentation time!
Course Projects
We will offer two kinds of projects:
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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. MS
students are not encouraged to take this kind
of project!
More information
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http://www.cs.uncc.edu/~jfan
Implementation Project
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Develop image/video analysis system using
Visual C++ and Java.
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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
Paper Presentation Project
If you are PhD student
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Present one research topic: you need to talk to
your professor to get relevant research papers,
prepare presentation slides & present in the
class.
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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.
Course Projects
If you do wonderful job on course project, you may expect
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Good grade even you may perform well
in final and mid-term tests
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Practical implementation means more than paper
work
Good recommendation letter for job
hunting: professor can only memorize
students with good performance!
Research position opportunities
Midterm & Final
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closed books and notes
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One page notes is permitted
Cumulative
No makeup
Bonus is expected
Key components for your final grade
If you miss 3 classes or more, you are not allowed to
take tests (mid-term and final)!
Suggestions from Instructor
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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
10-hours Golden Rules
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3 hours before class: go through the topics,
presentation slides and seek some relevant
online documents, …; ready to ask questions in
class
3 hours in class: listen to domain experts and
try to ask questions
4 hours after class: review what you have learnt
from the class, do your project and
assignments…
Who cares?
Who cares?
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Google Search Engine
Google Search Engine
Who cares?
The way to join them
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Good grade from class
More training on programming skills,
especially for multimedia analysis, indexing
and retrieval
Get recommendation from professor
Recommendation
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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
What areas we will touch?
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Computer Vision
Database
Information Retrieval
Machine Learning & AI
Visualization
Networks
Statistics & Security
What you may expect
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Start-up Companies
Many wonderful companies & start-ups come from course projects!
You could be the next one!
What you may expect
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Start-up Companies
Product search engine for
amezon.com, taobao.com
Using your smartphones to take pictures, then we will
find the cheapest ones for you!
What you may expect
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Start-up Companies
Image
Search
Engine
What you may expect
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Start-up Companies
Google Glass App: Google
glass may change world like
i-phone
What you may expect
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Start-up Companies
Digital Camera App: Sony
may sale digital cameras with
your media organization & search
software.
What you may expect
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Start-up Companies
Personal Computer App: IBM
Dell may sale PCs with your media
organization & search software.
What you may expect
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Start-up Companies
Automatic-Driving Car App: BMW
Tesla may sale cars with your object
recognition & navigation systems.
What you may expect
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Start-up Companies
Plant Species Identification: Your Kids will be proud of
you because you Know every plant species on the world
What you may expect
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Start-up Companies
Plant Species Identification: Your Kids will be proud of
you because you Know every plant species on the world
What you may expect
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Start-up Companies
Construction Safety Alarm: educators, government &
insurance companies may care
What you may expect
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Start-up Companies
Multimedia Search Engine
Google will definitely care!
What I or UNCC may expect
Do not forget to come back UNCC
& support our research!
Do your best & have fun!
Good students should be able to push your professor
to think and work harder not easier!