Did you experience how to build visual search accurately? If not, then how will you do it? Consider reading this post to get to know more.
Well, if you’re looking for something, someone may give a suggestion, “just Google it”. But web searching is an unplanned activity for all of us. That’s because most users instinctively hunt for search. And expecting to get a response after a click of a button.
How To Build Visual Search
Let’s take for example Google. Google has perfected the search and ranking process. How did they do that? That’s by making continuous iterations and tweaks to their algorithm. Additionally, they have at least one or two major updates a year. And this impacts the search outcomes.
For instance, “Possum” is one major update of Google. And this update is dependent on the searcher’s location. So if you’re searching for a place, then the nearest one will pop up as the best result. But, before this update, the search result usually gives a list of the first ranked places related to the one you are searching for.
Also, the quality of the search is a spectrum. Additionally, the more you search for one topic, the result moves from “good” to “better”. Then from “better” to “best”. Thus, we can say that search results improve over time.
Visual search, on the other hand, visual search is not different from the text search. With each upgrade, the results improve.
Challenges To Overcome
The Depth Of Subcategories
How does this work? The visual search can recognize various subcategories of the items we are searching for.
Let’s say for example that we are searching for jeans. Although the visual search engine may pick up different kinds, the search engine picks up only the ones we search for. Fortunately, some train the neural network powering the visual engine with multiple subcategories of articles.
As a result, visual search can now search for more articles and more items sold online.
The Model Posture
It’s the most common challenge faced by most visual search engines. For example, if the query is about a suit being worn by a model, then it will bring back less relevant results. Most likely, it will also bring back images with the same pose instead of clothes.
On the other hand, a visual search with hundreds of images each with different poses makes sure it recognizes clothes regardless of the pose.
Background Noise
Usually, if the picture you search for has a vivid background, the search result may only bring images of matching backgrounds instead of the object you desire to find. And it one of the challenges also.
Multiple Item Query
While searching for multiple articles, a search engine lets you select which pieces you want to search for. And you can do that by highlighting with boxes or by auto crop features.
However, most of the other existing solutions have results with entire combinations. And that result is often not relevant.
The Challenge Of Color Weightage
Let’s say you’re searching for an image of a floral pattern yellow dress. The actual result expectation might be red with a similar pattern. But, the visual search engine gets back results with more yellow dresses. But this challenge is overcome by the modern visual search engine.
