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Ikkyo Technology is a Kobe/Japan based startup focusing on Deep Learning and Machine Learning. Categorific is WebAPI that automatically connects and sorts out visual contents in the database based on their visual similarity.
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PROBLEM
• Web services are overwhelmed by the rapidly growing amount of user-generated visual contents
• There is no easy way for them to organise those visual data in their database other than worker-intensive text-tagging solution
SOLUTION
• WebAPI that automatically connects and sorts out visual contents in the database based on their visual similarity
• Cost efficient engine that charges the users based on the actual usage or the performance
CATEGORIFIC CASE I!!
To increase in-app stamp purchase for photo sharing app
Categorific+Case+I
• Too many screen transitions to find good fitting stamps for each pictures
• With Categorific, an app can recommend the most popular stamps based on the historical data that have the similar contents to a newly taken picture
Categorific+Case+I
(1) Open up DecoPic appCategorific+Case+I
(2) Select a photo from albumCategorific+Case+I
(3) Selecting "Stamp"Categorific+Case+I
(4) Selecting "+"Categorific+Case+I
(5) Look for a stamp for babyCategorific+Case+I
(6) Finally finds a baby stamp!Categorific+Case+I
(1) Open up DecPic appCategorific+Case+I
(2) Select a photo from albumCategorific+Case+I
(3) Baby stamp shows up!
Displaying!popular stamps!for baby pictures!
based on the!historical data
Categorific+Case+I
CATEGORIFIC CASE II!!
To add “search by image” option for e-commerce service
Categorific+Case+II
• Only text search is available to look for an item from the long list of web-shops and items
• With Categorific, “search by image” and “look for similar-looking items” functions can be easily added
Categorific+Case+II
• With “search by image,” users can find many similar-taste items with ease
• Users do not have to think about which text- wording would best describe a blue shirt
Categorific+Case+II
CATEGORIFIC CASE III!!
To sort out visual items based on content similarity for better user-
experience
Categorific+Case+III
• Stamps are simply listed, and it is too difficult for users to find what they are looking for
• With Categorific, an app can sort out stamps based on contents similarity for better user-experience
Categorific+Case+III
AFTERBEFORE
Categorific+Case+III
0
3,000,000
6,000,000
9,000,000
12,000,000
NOV DEC JAN FEB MAR APR
# of cumulative procesed images
12 millionWe processed!
!
!
images so far from !7 companies in!last 6 months
TRACK RECORD
ADRIEN!Hacker / Engineer!
!!
YUKI!Co-Founder / CTO!
!!!!
YOSHI!Co-Founder / CEO!
!!!
ICKO!Finance / Admin!
!!!
MARTIN!Full Stack Engineer!
!!
Team+Introduction
ADRIEN!Hacker / Engineer!
!!!
YUKI!Co-Founder / CTO!
!!!
YOSHI!Co-Founder / CEO!
!!!
MARTIN!Full Stack Engineer!
!!!
MATTHEW!Engineer!
!!
NAKASHIMA!Engineer!
!!
TANAKA!Engineer!
!!
YOKO!Engineer!
!!
NORIMATSU!Researcher!
Machine-Learning!!
HIO!Researcher!
Data-Mining!!
+!ENGINEER
RESEARCHER
ICKO!Finance / Admin!
!!!
WITH OUR SUPPORTING MEMBERS
Team+Introduction
APPENDIX I!!
Our real time motion capture technology for webcam recorded live
video!!
“Online Conference System 2006-148425 (P2006-148425A)“
Appendix+I
APPENDIX II!!
Developing Computer-Vision supported heavy-machinery video camera in order to enhance safety at construction sites as a
joint project with Kyoritsu Denshi Kogyo, the biggest crane-camera market share holder
Appendix+II
APPENDIX III!!
AI (both Computer-Vision and Machine-Learning) supported visual contents
moderation engine with our patent-pending “SmartAllocation" algorithm maximising human-computation efficiency to cut the
existing moderation cost by 90% at the most
Appendix+III
Filtering away inappropriate visual contents
• We reduced 90% of the existing human-only moderation cost for one of the world's biggest game companies without giving up moderation accuracy
• We did not directly apply computer-vision based moderation results, but used them to only maximise human computation efficiency instead.
Appendix+III
First Step!!
Developing suitable!classifiers for the!
targeted contents by!our CV technology,!
then applying them to!obtain ballpark results
Second Step!!
Efficiently managing!task allocation by!
continuously learning!from CV classifiers!and human output
“Smart Allocation“!Our patent-pending technology!
Third Step!!
Human computation!part can be conducted!
by crowdsourcing,!BPO companies, or!in-house community!management team
Appendix+III
