Machine Learning for Images - what's specific, various implementation of image recognition, what can be build using image recognition via machine learning. Image recognition frameworks, CNN, cuda-convnet2, coffee, torch7, theano, etc.

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Machine Learning for Images

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Huge amount
of user-generated
content
NOT
searchable
NOT
monetizable
It’s a big, big image world

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Image is a
matrix of pixels
(raster data)
What is specific: image recognition

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Recognition is sensitive to:
lighting
contrast
saturation
blur
noise
What is specific: image recognition

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sensitive to
geometric transformations
(scaling, translation, rotation)
occlusion
hard to
find optimal set of filters
What is specific: image recognition

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pixels
lots of data themselves
in spatial relationships
multiple levels and scales of interest
from low-level features such as texture to high-level
features such as composition
needs data-augmentation
compensate for sensitivity - training with blurred,
cropped, scaled, noised, etc
What is specific: ML for images

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huge architecture
(both deep and wide) - requires massive amount of
memory and processing power
inter- and intra-class variety
need to describe the universe (huge and diverse
datasets required to feed the data greedy CNNs)
takes time
10+ days for large architectures, even after 10x
reduction thanks to using GPUs
What is specific: ML for images

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cuda-convnet
python interface, fermi-generation nVidia GPU, no multi-
GPU support
cuda-convnet2
an upgrade to cuda-convnet, optimized for new kepler-
generation nVidia GPUs, multi-GPU support
caffe
deep learning framework, developed by Berkeley Vision
and Learning Center, big community of contributors
Convnet implementations for images

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torch7
ML algorithms, CNN extensions: fbcnn by Facebook,
used by Google DeepMind
theano
python library, open-ended in terms of network
architecture & transfer functions
…many others
Convnet implementations for images

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Imagga auto tagging

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CNN classifier with ~3000 everyday-life
objects
Imagga Auto Tagging

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Expansion with semantically related
concepts
Imagga Auto Tagging
business
meeting
presentation
laptop
desk
people

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semantic expansion
‘car’ -> ‘vehicle’ -> ‘mean of transportation’
feedback-loop
instant learning from user feedback, to be released in
May
custom training
with specific set of tags
Imagga Auto Tagging

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What can be built
with Image Recognition

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Personal Photo Applications
•Apps for mobile photos organization
•Integration in telecom solutions
•Cloud services for consumers
•Device manufacturers
http://getsliki.com

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Integration with analytics platforms

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Image Processing Services

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(Stock) Photography
http://wordroom.org

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Big image data management and
organization
Image driven platforms (DAMs)
Contextual advertising
Interactive/behavioural campaigns (adSense like)
User profiling
insight market, profiling based on image content
Interactive campaigns for brands
new ways to interact with customers
Use Cases

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Imagga APIs

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Imagga API: How it works

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Sign Up
www.imagga.com
Free Hacker Plan
to try out our image tagging API
Developer Plan
any of the APIs & within larger limits

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ML Internship
learn the AI magic
work with
machine learning
data collection
internal tool-chains
email us: jobs@imagga.com

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ML Meetup Sofia
machine learning meetups
bit.ly/mlmeetup
Sign Up

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Thank You
info@imagga.com twitter.com/imagga facebook.com/imagga