Visual Search: Why it is important and How it works?
Most often we are attracted by something we see, and we wish to buy something similar like that. But how to search for a thing exactly the way it is in front of you. Text will not justify the search. So why not take a picture and search for the most similar items like that. This is now possible with the new tech Visual Search.
Gone are the days when plain text used to be the only option to look for information on search engines. Nowadays, people have started using images as search query inputs to quickly find information related to the image or similar images. The retail industry will benefit the most from visual search. It helps to enable frictionless retail experiences to buyers by allowing them to shop the look. This means buyers can search with an influencer’s photo or with the snapped picture of a person and find the exact product or the relevant ones.
Importance of Visual Search
Visual search uses real-world images like screenshots, Internet images, or photographs as the stimuli for online searches.It has a host of applications in the eCommerce industry, particularly for fashion and home decor retailers. Visual search allows retailers to suggest thematically or stylistically related items to shoppers in a way they would struggle to do using a text query alone.
Retail brands are curating fashion collections, tailored to specific customer segments, that reveal the top trending styles and allowing us to buy the entire collection easily. Now, We can simply upload an image and get product recommendations that are visually compatible with the item in the image. It’s a much faster, more accurate, and engaging shopping experience. All of a sudden shopping is fun again!
How Visual Search Works?
Like text-based search, visual search also interprets and understands a user’s query which in this case is an image, and finally delivers the relevant search results. AI is used to detect elements in an image which are then used to identify and show similar images. Text-based search forces people to think hard to get their search query right and find what they are looking for. But visual search powered by AI helps to interpret images and take visual cues from it thereby reducing the burden for the searcher.
The technology uses visuals as ‘queries,’ and analyzes shapes, colors, and sizes to find the best matching search results. Image recognition is what allows a computer to actually identify, understand, and categorize specific objects within an image. To do this accurately, visual AI requires training, just as a young child needs to be taught that “4” means “four” and “?” means “question.”
Researchers and engineers train a visual AI engine to interpret and identify specific elements within images by introducing them to as many categorized and labeled images as they can. The AI engine processes and learns from every pixel in each image so it can refine and expand its understanding of different objects over time.
For example, engineers can feed a visual AI engine thousands of images of sofas in different styles, sizes, and colors. Now, when you upload an image to the visual AI engine, it automatically compares each pixel to every sofa it’s ever seen. This means that when you input a photo of a room via computer vision, the AI engine can point to the sofa, know what it is, and identify its tiniest characteristics in an instant. These are some of the requirements to implement Visual Search
Structured Data
The use of structured data helps to take the visual search experience to an entirely new level. It helps the visual search engines to return more relevant results by allowing them to more accurately scan web page content.
Image Categorization
Tagging and annotation of images help to improve the image search experience. Labeling and categorization of images help to surface the best images for searchers and make it easier for them to find what they are looking for.
Alt Attributes
In addition to improving the accessibility of images, a descriptive alt text helps the search engines to understand the image better and provide the relevant search results.
Image Quality
The quality of the image has to be good enough for the visual search engines to see the individual components within an image and the image shouldn’t be pixelated.
Wrapping up
Although still in its early days, visual search is developing at an incredible rate. Top industry players are already investing big dollars in research and experimental development which further speeds up the process. Digital marketers should keep an eye on this trend. By adapting to consumers’ natural preferences for visual content, brands are bound to succeed.
At TagX, we combine technology with human care to provide image annotations and video annotations with pixel-level accuracy. Our data labelers maintain quality while processing & labeling the image data which can be used efficiently for various AI and ML initiatives. As the search expands beyond its traditional forms, and new technologies continue to be introduced, it is important to understand and take advantage of it as soon as possible.
Originally published at https://www.tagxdata.com.