AI and Deep Learning Enhancing Visual Search

When we mention the term ‘search’, it typically conveys finding. Counting what technology changed over the years, search serves as the very beginning of the online ecosystem that empowers billions of businesses across the internet. Primarily the main types of search forms are textual and image. And, the most common instance of visual search can be found with the world’s biggest search engine - Google. When the user enters a query, the search engine algorithm maps the textual request to available images and displays it to the user searching.

Over time, search has now been powered using AI and visual search deep learning techniques. A fair amount of machine learning involvement has gone into all forms of online search methods.

Applying Visual Search with AI and Deep Learning

Over the period of years, visual search has changed forms. We can see specific service models which demand visual search to be part of their platforms. A lot now depends on human search behavior online and depends on what the user finds more clickable than the other. Human search behavior or modeling has been based on principles of cognitive neurosciences and psychology, extensively applied while creating visual search-based visual fields.

Whether the visual search works on an integrated, multi-layer platform or on an internal search platform of a smartphone application, with deep neural networks, the algorithmic capability of search results drastically varies.

The deep learning model on analyzing the performance of human search on realistic web pages or visual fields can perform many functions that just analyze the visual search parameters. These visual fields powered by deep learning techniques applied at the backend can do face recognition, read and detect objects, as well along with providing deep insights into what produces user action when the target is displayed to the user. Here, research from cognitive neurosciences helps in exploring the target and the candidate stimuli in the visual scene. At the same time, there are computational models which deal in which the attention specifications are around top-down and bottom-up processing of visual search, based on the guided search model. Of this main model, two more sub-models - preattentive and post attentive models which are built on colors, size, and orientations. These attributes are detectable once acuity thresholds are reached. Such models are integrated for designing and developing graphical user interfaces across industries that are oriented towards the visual interaction of users.

Also Read: How Visual Search is Helpful for Ecommerce Industry?

Visual Search Use Cases


Significant research has been conducted to define what grabs user attention when they’re either finding a piece of information or making the step-wise transactions to subscribe to a service. If we discuss this, the real online world which includes many diversions for users to look at and act upon, guessing the user interest can be highly complicated. No matter the perception design on a webpage or interface, an unestimated percentage of users will still not click on any options and refrain from buying or clicking on any web elements. This is where deep learning and AI-based techniques can bring predictability. The convolutional neural networks have effectively been able to figure out what will be based on the available data of users’ behavior.

A mesh of the training data and interface design principles can help brands’ become undisputed and iconic fashion identities with global reach.

Also Read: How to Validate Machine Learning Models: ML Model Validation Methods?

Limitations and the Future Ahead in Visual Search

Visual search is still being explored. More research and findings are required to implement visual search in multiple domains; the applicability of which will depend on the product structure design. Towards implementing such evolved interfaces and design attributes, availability of training data for learning of machine learning models and consumption by Convolutional Neural Network is the main challenge.

We have an evolving online ecosystem. And billions of users with diverse cognitive and perceptive abilities are using smart applications. In the implementation of visual search-based AI solutions, another challenge remains to mold the existing design principles as per the shifting online behavior. To map the frequency of cognitive behavior would demand enhanced capabilities in the near future. However, creating human-machine interfaces powered with visual search findings can’t be just written off. For visual search, working out the vulnerabilities will also need significant volumes of research; a detailed dialogue on which shall come up soon.