- Artificial Intelligence
- Artificial Intelligence
Manual search is tedious and only helpful when you know exactly the name and model of a product. That’s what most customers claim about their experience finding a particular item or its alternatives. Indeed, the process is incredibly time-consuming and is complicated with other common online shopping problems (long delivery, faulty search engine, insecurity, etc.).
To simplify the process of online search, companies like Google invest in developing AI-powered solutions such as image search. It uses image recognition algorithms to analyze a picture and find similar products for sale.
Amazon was one of the first online retailers to start incorporating AI. Back then, it helped, to a certain extent, in designing visual recognition for the brand. AI-powered screenshot search extension – StyleSnap, released in 2019 by Amazon, is regarded as a highly effective online shopping assistant feature.
For starters, image recognition is an AI-powered set of algorithms and protocols incorporating multiple visual data analysis techniques. In simple words, image recognition is the ability of a software solution to identify the requested objects.
ML in the fashion industry mainly relies on the computer vision (CV) system. It is a computer science field focused on enabling AI-based software to retrieve meaningful information from visual materials. Some of the typical CV tasks include:
AI-driven image processing makes possible face recognition as well. As such, you can use your phone’s camera to unlock it, try on Instagram masks, and many more. It is possible to distinguish two major ways of image recognition implementation in the fashion industry. These include visual search and smart recommendations. Let’s review both of them in detail.
Visual search is the AI-driven technology that incorporates the techniques of visual recognition for images, video, and 3D. It allows computers to scan an image uploaded, identify objects detected, and categorize them. Then, a program matches the found items with ones in a database according to the following key factors listed in order of decreasing importance.
For example, if you upload a screenshot of a pillow, a system will define it as a pillow first. Then, algorithms will figure out if it is a decorative, regular, or medical one, its size next, a style, whether it is a luxury or a simple pillow, and finally, its color. Every pixel of this pillow will be matched with all the pictures of pillows in the system to find exactly the same or similar ones.
What’s also great about visual search is that it is based on visual content machine learning techniques (ML). It means that the more images the system processes, the better, more accurate result it delivers. Because of continuously better outcomes, demand for visual search rises exponentially.
Accurate recognition of clothes is an integral part of the visual search in the fashion industry. Basically, AI algorithms are specifically adjusted to detect clothing only. It helps not to distinguish irrelevant objects accidentally.
Visual search extensions that online fashion retailers offer to employ can detect clothes and match their details with the internal databases. As a result, a user receives links to web pages to review exactly the same or similar item. Additionally, a customer may be shown information for a specific item directly on an image. Clothes detection is a simple and convenient tool facilitating online shopping.
Generating automated, personalized suggestions is the second major way of implementing visual recognition fashion retail. Smart recommendation system incorporates data that was previously obtained from visual search. Then, it creates unique sets of suitable fashion choices for a customer to consider.
The process is grounded on multimedia data mining that processes large volumes of information and generates smart proposals. In such a manner, a fashion retailer’s virtual search system tracks down similar items. It helps users find in the virtual space other items that customers may be interested in. Similar and personal recommendations are two types of suggestions the smart recommendations system can generate. Let’s review them.
These are similar to visual search extensions’ suggestions. But, they are not limited to information received from a single screenshot uploaded. The system gathers data for all the searches done and generates more accurate recommendations for finding similar items.
The fashion retailer website itself typically shows substitutes of items you have already searched for. Suggestions may be pretty useful and accurate, enhancing your shopping experience.
Such smart suggestions are not that different from the previously described ones. But, they personalize the selection of items even more, so users may be provided unique advice for future purchases. The system is learning deep representations for visual recognition constantly. Based on the outcomes, it considers the personal specificities of a user and incorporates results in the recommendation system.
As an example, if a customer has already purchased clothes, data for an item’s size is noted by the smart system. Next time this particular customer will be shown a recommendation, an item’s size will likely match their preferences. The system processes colors, styles, and other essential aspects similarly.
The process of clothing detection is grounded on creating and adjusting a model – a specialized system for particular object searching. Such models may be trained to recognize objects as they appear in social media, fashion modeling, and other contexts. The way clothing detection algorithms are integrated into such a model can be divided into four following steps:
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We have already mentioned that big online retailers were first to incorporate visual recognition in their websites’ extensions. Two such cases are Style Match, an internal search engine by ASOS – British fashion retailer, and StyleSnap addon by Amazon – the world’s largest U.S.-based marketplace. Both systems are perfect for the visual search of items similar to ones detected in an uploaded screenshot.
Style Match enables customers to easily upload a picture of the desired item so the smart search engine can find exactly the same or similar fashion goods. The extension was released in August 2017.
Technically, StyleSnap is not any different from Style Match. But it is enhanced and a bit complicated with automated tagging of items in a photo. The search capacities of Style Match are extended and go beyond clothing only. It is justified by the higher range of products Amazon offers customers to purchase. These include fashion items, furniture, electronics, and many more. Users have employed the feature since April 2017.
The kicker of the visual recognition system is automated product tagging. It is simply labeling a product’s image based on detected visual attributes. Then a processed image is attributed to a particular catalog, from which it can be retrieved and shown to a customer who has searched for a similar item.
Manual categorization is no longer needed as digital systems perform the process more effectively. They decrease the number of errors in tagging, which is helpful both for visual recognition and for catalog or inventory management. Automated product tagging ensures that stocks are not oversupplied or undersupplied. Basically, it is a cost-effective solution to multiple inventory management issues.
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