The rise of smartphones, inexpensive cameras, and enhanced image recognition thanks to the deep learning-based approach opened a new era for image recognition. Companies in different segments such as automotive, gaming, and E-Commerce are implementing this technology.
While choosing an image recognition solution to find photos, its accuracy is the most important factor, however continuous learning, speed, and flexibility are also important criteria depending on the usage.
Tech giants like Amazon and Google as well as small startups like Clarifai and Reverse picture search are offering search by image services using image recognition tools.
What is Reverse Image Search?
A precise object or objects in a picture can be differentiated using the image recognition technique.
Image recognition is a set of algorithms and different techniques to label and classifies the elements inside of an image. Image recognition mainly focuses on the contents inside of an image. Image recognition models are trained to take an input image from the user and output formerly classified labels that define the image.
Image recognition is an imitation of the techniques that are used to detect and classify different objects.
How does Reverse Image Search work?
Model training is essential for a search by image model to work. Deep learning methods are currently the best performing tools to train image finder models.
For a picture finder tool to work, first, there should be a database. Consider an example of a newborn baby, for the baby to recognize the objects around him, the objects should be introduced to him firstly by his parents.
The process is similar for reverse image search, there is a database setup, and using deep learning techniques, the model must be trained to perform a search by image.
An image is a bunch of different pixels to a computer. To make an evocative result from this data, it is essential to extract certain features from the image. This process is called feature extraction.
It allows specific patterns to be represented by specific vectors. Deep learning methods are also used to determine the boundaries of these vectors. At this point, a database is used to train this model, and at the end, the model predicts a certain object and labels it as the new input into a certain class for search by image.
Why is Reverse photo lookup relevant now?
Image recognition allows expressive data to be extracted from an image and therefore has several applications. However, reverse search by image accuracy improved recently, making image recognition irrelevant for the past ten years.
These factors contributed to the increased worth of image recognition:
- Increased efficiency of deep learning
- Reducing camera size and price, coupled with increased smartphone infiltration.
- Image-based social media: thanks to smartphones and image sharing social media platforms, images are thriving.
According to MarketsandMarkets Image recognition market is estimated to grow from 15.9 billion dollars to 38.9 billion dollars at the end of 2021, at the CAGR of 19.5% during the forecast period.
Visual search Statistics and Benefits
As the search expands beyond its traditional forms and new technologies continue to be introduced, it is important to acknowledge and take advantage of it as soon as possible.
With 35% of marketers planning to optimize their sites for search by image shortly, getting ahead of the competition is better done early rather than late.
Discovered by the Next Generation
With 60% of Generation Z now discovering brands solely through search by image and social media applications, and almost 70% of them planning to purchase directly from these platforms, this is the best time to get your brand discovered outside of traditional stores.
Marketers are seeing Pinterest’s potential for reaching consumers as they are considering products. Placing ads is one way they are inserting their brands into that interaction. Pinterest Inc. generated over $1 billion in ad revenue alone in 2020.
Increase your revenue.
After some investment, sites can look forward to the opportunity of massively increased revenues. According to research early adopters and optimizers of both visual and voice research can find their capitals increased by almost 30% at the end of 2021.
Another article in Forbes Magazine predicted that by the end of 2021, the visual search market was set to be worth no less than $39 billion – although the results are still unknown on how much more it can grow.
Visual search and E-Commerce
An article in Forbes magazine stated that the visual search market is expected to grow up to 39 billion dollars by 2021.
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The main reason is search by image integration with online shopping and customer habits are diverting towards this way. And with increased social media platforms supporting visual search it is expanding faster than ever before.
Visual technology has been expanding in recent years. Image search engines are getting better, and companies are adopting it to solve problems for their customers. And with 32% of customers regularly using image searches in their daily life, it is becoming a necessity.