Mobile Recognition
Going Mobile: The Future of Mobile Shopping
Mobile is critical to retail in the coming years. Forbes said, “87% of connected devices sales by 2017 will
be tablets and smartphones.” Mobile is disrupting commerce. Physical shop real estate is shrinking, so e-commerce is more important than ever.
Only 4% of budgets go into mobile but it’s where 20% of
consumers time is.
8 in 10 smartphone users will use their smartphone at some point during the purchase process.
Nowadays appr. 29% of all online purchases are made via
mobile devices. Mobile is where the growth is and where it will
continue to be. But brands aren’t moving as fast
as consumers.
Sources: London e-commerce expo 2014-15; Forbes; MobPartner; Mobile Future Institute;
Amazon research.
The number of purchases made with mobile recognition
doubles every two months.
In 2015 more retailers will realize the benefits and
advantages of the visual recognition technology for both
consumers and their respective businesses.
Going Mobile: Image Recognition Technology
Image recognition for mCommerce and eCommerce is
about delivering instant access to a desired product for
shoppers, and increasing click through rates, customer
engagement and average basket size for retailers.
It's just as easy, as using a camera on a mobile device. All a consumer has to do is to take a picture of any product
they liked with a mobile gadget. Image recognition
application will then automatically find identical or most
similar offers on a retailer's catalog.
Image recognition application lets users easily shop items
directly off the image, without having to scan bar codes,
look up links or search for product online or in store.
Image recognition helps to capitalize
on the smartphone/tablet’s potential to enable
consumers make impulse purchases.
Studies show that the idea which drives the impulse
purchase (you take a stroll and see astonishingly! looking
pumps on a lady passing by) remains in the consumer’s
brain for only a couple of seconds. Mobile image
recognition technology offers the user the freedom to
satisfy the impulse wherever he or she is, otherwise they will
forget it very quickly.
Going Mobile: Kuznech Mobile Recognition
Since people are moving more and more into mobile in their
way of online search, mobile visual search has become one
of big targets for most companies that concentrate on
computer vision. Kuznech Mobile Product Recognition is a technological
breakthrough for eCommerce and mCommerce. The
system automatically recognizes products in a view of a
mobile camera, determining their type and finding the
same/similar products in a retailer’s catalog. Use a mobile camera to simplify purchases of
almost any kind of product — from apparel and
accessories to food and home appliances.
Kuznech's technology has modeled the human visual cortex
in its propensity to pick out many points of interest of the
object: it does not scan objects from top to bottom or from
one side to another, but creates a "digital fingerprint" (see
slide: Vocabulary) of an object. Thanks to this, Kuznech's
mobile recognition technology works so quickly and is so
easy to use!
Now your customer can use a mobile camera to simplify
purchases of almost any kind of product — from apparel
and accessories to food and home appliances.
Kuznech Mobile Recognition:
How It Works
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Mobile Recognition by Kuznech
simplifies online purchasing to only three steps:
Smartphone – Recognition – Purchase.
A user just points the mobile camera at a physical
object (any product), and it will be instantly
recognized by Kuznech’s mobile API. 1
The system processes the query, recognizes the
identical items or picks the most similar
product out of the retailer’s database.
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One-two-three, the consumer makes a purchase
directly from the gadget wherever they may be.
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Kuznech Mobile Recognition:
How It Works
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You integrate Kuznech’s Mobile Product
Recognition API into your current application (or
Kuznech can develop an app for you on demand).
Mobile Recognition by Kuznech
simplifies online purchasing to only three steps:
Smartphone – Recognition – Purchase.
Kuznech technology processes image input,
analyzes it by interest points, and creates a
fingerprint of the detected product that is then
transferred to the database.
The fingerprint of the source product is
compared with the fingerprints of other products
in your database, and the most similar product is
picked.
This product page appears on the screen, and a
purchase can be made immediately.
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Kuznech Mobile Recognition:
Inside the Technology
Kuznech uses a modern visual-recognition technology to
detect and identify products on snapshots. To do visual
search and recognition, we apply a wide scale of methods:
hashing, deep convolutional neural networks (CNN), deep
learning, clustering and classification (see slide: Vocabulary). WHAT'S INSIDE KUZNECH TECHNOLOGY:
- On images taken by a mobile camera, Kuznech Сlassifier
recognizes most popular product categories and detects a
product. - Enhanced algorithms quickly separate the detected
object from a background. To do visual search and recognition, Kuznech
applies a wide scale of methods:
hashing, CNN, deep learning,
clustering and classification.
- Kuznech Core Engine creates a fingerprint of the detected
item, performs visual search and compares the recognized
item towards other products in the retailer's database. - Kuznech SimilarSearch Engine picks out most similar
items and sorts them according to relevance scale.
Kuznech Mobile Recognition:
Features
High upload & response speed. Our engineers effectively
combined hand-crafted methods with self-organized
structures to provide fast and accurate search and model
comparison. Thanks to this, the image’s fingerprint is ultra
small, about only 2-5 kb, which is crucial for data transfer in
3G networks.
Say NO! to wasting mobile traffic on a large digital fingerprint
and YES! to high upload and response speed.
Advanced detection and search technologies allow us to
provide services, which do not require active user
involvement (for example, manual category predefinition or
target localization) in the search pipeline. We combined hand-crafted methods
with self-organized structures to provide fast
and accurate mobile recognition service.
Robustness to technical challenges: Kuznech Mobile
Product Recognition Technology can effectively deal with
technical challenges, typical for mobile image search, like:
different quality cameras, shifted color balance, blurring, and
over-exposure.
Scalability: Kuznech API libraries are built on JAVA/C++. The app can be created for any of the leading platforms: iOS,
Android, or WindowsMobile.
Kuznech Mobile Recognition:
Your Benefits
Connect the physical world of the customer with the
digital world of your e-store: capitalize on mobile devices’
potential to help consumers make impulse purchases
wherever they may be. Respect your users, and they pay you back. Kuznech
Mobile Product Recognition technology offers exceptionally
high potential for user engagement and retention, helping
you increase your click through rates, customer
engagement, and average basket size.
Keep it simple: Mobile Product Recognition simplifies online
purchasing for almost any product type to only three
steps: Smartphone – Recognition – Purchase.
Kuznech Mobile Product Recognition:
we are here for you. To win.
Vocabulary
Digital fingerprint of an image
Convolutional Neural Networks (CNN)
A coded string of binary digits generated
by a mathematical algorithm, uniquely
identifies an image. Just like the analog fingerprint of a person,
a fingerprint of an image cannot be
reconstructed from any other digital
fingerprint.
CNN are useful to recognize visual
patterns from pixel images with minimal
preprocessing. They can recognize
patterns with extreme variability and are robust to distortions.
Hashing
Hashing in general is a useful way to
reduce a huge amount of data to a
short(ish) number that can be used to
identify that image.
Hashing is used to index and retrieve
images in a database because it is faster
to find the item using the shorter hashed
key than to find it using the original value.
Vocabulary
Deep learning
Clustering
Classification
A field of computer science that seeks to
mimic the human brain with hardware and
software. Kuznech Visual Search and
Recognition Engine is powered by
convolutional neural networks, a type of
deep-learning technology.
Cluster analysis, or clustering is the task of
grouping a set of objects in such a way
that objects in the same group (called a
cluster) are more similar (in some sense or
another) to each other than to those in
other groups (clusters).
In machine learning and statistics,
classification is the problem of identifying
to which of a set of categories a new
observation belongs.
Kuznech Mobile Recognition:
Nice to Meet You!
Learn more about Kuznech Mobile Product Recognition:
http://kuznech.com/products/mobile-recognition/
Feel free to contact us: http://kuznech.com/contacts/
[email protected]
Interested in Mobile Recognition?
We have a lot in common!
Shall we talk it over? :)