Fashion recognition apps — often dubbed “Shazam for Fashion” — promised to make the entire world shoppable. Anytime, anywhere, a user would be able to snap a piece of inspiration — a sharply cut coat on a passerby, or a fetching mini-dress in a magazine — and sophisticated “visual search” technology would identify and retrieve a link to the item, available to instantly buy, thereby radically shortening the path from inspiration to transaction. Or so the pitch went. Over the last two years, investment has poured into start-ups building fashion recognition apps. But are they working?
As for now these apps not only couldn't find the exact same clothes but most of the times even are not able to propose similar color or type.If there is a fundamental problem with fashion recognition apps, it’s the current state of the underlying technology itself. Dr Anil Anthony Bharath and Jeffery Ng at Imperial College London and a leading developer of image recognition technology say that there are techniques that they’re using going forward to make recognition results better, like machine learning, deep learning, neutral networks. For the moment, however, image recognition technology is simply not good enough to differentiate between fashion items, which often have subtle but critical differences in cut and color.
Daniela Cecilio, CEO of ASAP54, a fashion recognition app she founded in 2013 told that exact recognition is not feasible in the near future despite the fact that ASAP54 employs teams of human stylists to enhance its automated search results.
Another problem is payment as it has also been a source of friction for users. Indeed, for fashion recognition apps, which pull search results from a wide range of retailers, the journey from picture to purchase is far from smooth. Once they have found an item they want to buy, users are often redirected to retailer sites and left to contend with whatever payment processes they have in place, rather than being able to complete a purchase within a given app. Snap Fashion reports that only 2 percent of its users actually manage to make their way down the sales funnel and complete a purchase.
But do we have anything better than these apps? Actually, yes. The first approach is to shop using when the image recognition technology which is deployed at the level of a specific retailer. Visual search results come from a limited universe of products, make accurate results more likely. It’s not quite as exciting as making the entire world shoppable, but it works.
For example, Neiman Marcus launched ‘Snap. Find. Shop,’ a visual search feature on their mobile app powered by Slyce. Customers are able to take a picture of a bag or shoe and, then, with no further work from the user, they get the closest comparable match from the Neiman Marcus catalogue. Then, they have the ability to do a one-click checkout and purchase that item. The same technology can also help users locate similar items in-store, or while browsing at the stores of competitors.
The second approach which serves better monetising desire by giving consumers exactly what they want, at the very moment that they realise they want it, could be fashion recognition technology integrated into large social platforms such as Instagram or Pinterest. These sites are awash with fashion imagery, from street style images to product shots. And with over 200 million users, a platform like Instagram could leverage learning algorithms that return better results with each use, in a way that start-ups simply cannot.
Source: http://www.businessoffashion.com/2014/11/trouble-fashion-recognition-apps.html
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