ABOUT EMPORA.COM

Empora is the fashion search engine - shop from thousands of the most fashionable stores and brands in one place.

From designer treats to high street chic, you'll find items to create any look based on your own personal style and budget.

How does it work?

Easy navigation: Search thousands of stores and brands at once, filtering by designer, brand, store, price and colour.

'More Like This': See similar styles by clicking on 'More Like This'. Our unique visual search technology returns similar items by style, colour, and cut.

Get the celebrity look: Browse the latest looks from trend-setting celebs and international catwalks - all you have to do is to set your budget!

HOW DO WE DO IT

The techniques we draw on in order to achieve similarity search come from the fields of machine learning, image processing and computer vision, three vibrant research disciplines.

The idea is simple: instead of relying on tags that may or may not have been added to the images, we try to understand what the image depicts based on a pixel-wise analysis. Colour and texture properties are extracted as well as particular objects. This automatically derived knowledge can subsequently be exploited to help users query and navigate more efficiently through large collections of fashion items. Our research group is led by Dr Daniel Heesch, one of the founders. At the core of our activities lies the problem of image understanding, a challenge that has occupied and consumed generations of scientists. Particular areas we are interested in are large-scale multi-class classification, new methods for unsupervised object segmentation, and the development of new visual features for shape analysis. The work is a mixture of long-term foundational research and applied problem solving.

If you want to know whether you can use our technology or our applications of it, contact us!

A sample of recent publications and talks by group members is given below.

A survey of browsing models for content based image retrieval (2008) Multimedia Tools and Applications 40, 261-284 (Springer) paper at:
http://www.daniel-heesch.com/papers/heesch-08a.pdf

Two step relevance feedback for interactive sense disambiguation in image retrieval (2008) International Conference Visual Information Systems paper at:
http://www.daniel-heesch.com/papers/heesch-08b.pdf

Image Search for the World Wide Web (2009) Invited Lecture (IET London, 20th Jan) slides at:
http://www.daniel-heesch.com/papers/heesch-09a-slides.pdf