CUbRIK Summer School 2014
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CUbRIK Fashion Trends
Bringing innovation to SMEs in the Fashion market
Claudio Massari – Innovation Engineering srl
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Agenda
Innovation Engineering
The Fashion domain
The rationale behind the application
The CUbRIK approach
Main challenges
Conclusion
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Innovation Engineering
Private Italian SME
Specialisation in the design & development of advanced IT solutions supporting knowledge extraction and management within organizations
Main Clients: Large Corporation, SMEs, Public bodies;
Involved in several R&D European and National projects
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The Fashion domain
The Fashion domain
Fashion is a multi-billion Euro industry
representing about 3% of the EU GDP
90% of the organisations are SMEs
The fashion domain is characterized by:
Strong investments in new processes, new materials, new products
High seasonability: quick adaptation to the market requirements
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The rationale behind CUbRIK Fashion Trends
SMEs needs
SMEs are constantly seeking for innovative
products and new trends
Data for Trends Analysis are collected manually:
surveys, interviews
Expensive Analysis performed by specialized agencies
and consultants
Gathering of the data is expensive
Analising Trends is expensive
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SMEs’ needs
Why doesn’t your company carry out trend analysis when planning new products
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The rationale
Aim of the CUbRIK Fashion Trend
Supporting European SMEs in the fashion sector to:
Exploit the potential of the new technology
Spot trends as soon as they evolve
To identify potential customer preferences
Get feedback from and learn about the needs of fashion consumers.
To trigger new trends through open innovation with SME’s own target groups
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CUbRIK Fashion Trend main features
Trend analysis
What may SMEs obtain?
Trend analysis based on:
Garment category
A sample image
Time and spatial constraints
Results obtained:
The most popular colors, textures
An insight on the most popular images
The best combination of colors
Need addressed:
Affordable Trend analysis
Spot of the most popular colors
for a garment
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CUbRIK Fashion Trend main features
Create an outfit and ask feedback
What may SMEs obtain?
Create and test an outfit based on:
A sample image
A set of similar images
Time and spatial constraints
Results obtained:
Creating an outfit for further analysis
Feedback about the outfit from their
network
Need addressed:
Affordable perception of the customers feeling
Support for realising collections (producing, buying, distributing)
CUbRIK Summer School 2014
Ask for feedback
SMEs obtain to:
Test a garment based on:
A sample image
Results obtained:
Receiving feedback about the
garment from their network
Need addressed:
Testing new product
Affordable perception of the customers feeling
Support for realising collections
(producing buying, distributing)
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CUbRIK Fashion Trend main features
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The CUbRIK pillars
Human in the loop
Gathering users’ generated content
Adopting a mixed solution: automatic analysis and Human in the loop approach
User preferences identification of the from the Social Network
Results:
Detecting trends on user preferences basis
Multimedia search similarity supporting SMEs in feeling the market
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Usage of mixed crowdsourcing resources:
Crowdworkers
Players of Games With A Purpose – GWAP
Social Network users (preferences) and SMEs’ network (feedback)
Rationale
The “human touch” enhances the results and carries out key tasks
The “human feeling” is crucial in the fashion sector:
SMEs require analysis based on human feeling
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The CUbRIK Human in the loop approach
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Gathering images
Usage of the user’s content: Social networks are a tangible resource: users generate large
amount of data
High usage of the Social Network and the games: in 2011, they were the 1st and 2nd online activities, surpassing email [Nielsen]
200k images are crawled each week in Twitter
Main issue
Mixed quality of the images
Affordable huge quantity of data for SMEs
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The CUbRIK mixed approach
Software analysis + Human in the loop provide SMEs with: Better Image identification
Is this Fashion? Automatic crawling is not capable to answer
Enhancement of results
More precise feature analysis results
available for the Trend detection
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The CUbRIK mixed approach
Just humans can correctly understand similarity:
Are these images similar? The human perception of the similarity «dimensions» provides
SMEs users with similarity point of view
Skirt length
Pattern
Cut
Trendiness
Casualness
Suitability for a type of weather
SMEs are provided with results enhanced by humans
Vergleichen von Kleidungsstücken
47
Ähneln sich die Röcke (Farbe, Schnitt, Länge, …)?
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Gathering preferences
Users preferences on Twitter (based on users activities)
Users preferences from videos
(which are the preferred
keyframes?)
Gathering feedback from your customers’ network
Do you like this image or this outfit?
SMEs can rely on users/customers preferences
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The main challenge
The main challenges in the Fashion trends detecting:
Generation of the dataset (identification of fashion images)
Identification of the garments
Analysis multimedia features (color, texture)
Popularity detection
Similarity identification
The CUbRIK Approach:
Crawling and human in the loop for filtering fashion images
Automatic and manual (GWAP) segmentations of the garments
Automatic processing of images (features extraction)
Popularity identified through users activities in Social networks
Human in the loop for similarity perception identification
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Benefits Innovation potentials
Innovative tool providing SMEs with a affordable market trend analysis
User “fresh” based generated contents. Affordable
Easy detection of popular behaviours/trends and colour/texture by location/season
Identification of similarities and recommendations provision
SMEs (small actors in the fashion sector, B2C):
Get over budget constraints for wide market analysis
Switch from an analysis based on inner personnel experience to an affordable analysis based on their potential customers and trends
Large organizations (buyers, distributors, producers addressing general public)
Tackle the need of detect and pilot trends obtaining affordable results in order to manage production, buying and distribution
Obtain an additional/alternative tool to retrieve trend analysis;
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THANKS!