What is Visual Search Technology and How Does it Help Users Get What They Are After

How Visual Search Technology Works  

What is visual search? The concept of visual recognition, also known as visual perception, refers to the ability of computers to understand and interpret visual information like humans do. It involves recognizing objects, scenes, text, patterns, or even actions depicted in images or videos for further exploring visual search functionalities. 

Examples of visual objects that image recognition software can identify include: 

• Faces 
• Vehicles (e.g., cars, bicycles, airplanes) 
• Landmarks (e.g., famous buildings, monuments) 
• Plants (e.g., trees, flowers) 
• Clothing and fashion accessories 
• Signs and symbols 
• Handwritten or printed text. 

Image recognition technology has various applications across industries, including healthcare, automotive, retail, security, and entertainment. It powers technologies such as facial recognition systems, image-based search solutions, product identification in e-commerce, and content moderation on social media platforms. Let’s look at how it differs from the traditional text-based search. 

Criteria	Text-based search	Visual search
Speed	Traditional text-based search requires users to manually input search queries, which may take time, especially for complex or lengthy queries. Users must type accurately and often need to refine search terms to get desired results.	Visual search lets users quickly find relevant information by simply uploading an image. Recognizing and matching visual content can be faster than typing out search queries.
Convenience	Text-based search is convenient for users who are proficient in expressing their search intent through words. It’s suitable for searching textual information or when users have precise keywords in mind. However, it may be less convenient for users who find typing cumbersome or are unsure of the appropriate search terms.	Visual search offers convenience by eliminating the need for users to articulate their search intent into text. It’s particularly useful when users struggle to describe what they’re looking for in words, such as when searching for visually distinctive or hard-to-describe items.
Accuracy	Text-based search generally offers high accuracy when users provide clear and relevant search queries. Search engines rely on algorithms that match keywords to indexed content, resulting in precise and targeted results. However, accuracy may decrease if users use ambiguous or misspelled terms.	Visual search accuracy can vary depending on factors such as the image’s quality, the object’s complexity, and the sophistication of the underlying algorithms. It may struggle with recognizing highly similar objects or items with ambiguous features.

As navigating through visual data is more convenient for various reasons mentioned above, the popularity is increasing – 62% of millennials prefer visual search over all other methods. Therefore, companies increasingly utilize visual discovery tools to address industry-specific challenges. Let’s examine how the application of AI in visual search works in practice. 

Applications of Visual Search Technology  

Case 1. Streamlined product discovery in E-commerce 

Challenge:  

A large e-commerce company faced the challenge of helping users find products quickly and easily amidst a vast inventory. Traditional text-based search often led to ambiguous or misspelled queries, resulting in unsatisfying search results and customer retention. 

Solution: 

The company implemented custom visual search software that allowed users to search for products using images instead of text. This visual search tool leveraged advanced image recognition algorithms to analyze visual features and match them with similar products in the inventory. Enhancing user experience with visual search allowed for the following benefits: 

• Improved product discovery: Visual search significantly enhanced the product discovery experience for users by allowing them to find items based on visual similarity rather than specific keywords. 
• Increased conversion rates: By presenting users with visually similar products, the company saw higher engagement and conversion rates as users were more likely to find products that matched their preferences. 

Case 2. Faster patient data processing in healthcare 

Challenge:  

A healthcare organization faced the challenge of efficiently analyzing large volumes of patient data from various sources. Manually extracting relevant information from medical records was time-consuming and prone to errors, hindering timely diagnosing. 

Solution: 

The organization implemented custom visual search AI software that utilized object recognition and natural language processing (NLP) techniques to extract and analyze textual information from medical documents, such as patient records, lab reports, and imaging scans. Healthcare providers noted the following benefits: 

• Accelerated data processing: Visual search tools automated the extraction and processing of patient data, significantly reducing the time and effort required for manual review. 

• Improved data accuracy: By leveraging visual search engines and NLP capabilities, the software improved the accuracy of extracting relevant information from medical records and ensuring data integrity. 

Case 3. Improved reader engagement in publishing 

Challenge:  

A publishing company struggled to engage readers in a highly competitive content niche. Legacy text-based content formats struggled to capture and retain readers’ attention, leading to lower engagement rates. 

Solution: 

The company introduced custom visual search software that enriched the reading experience by harnessing visual cues for better search outcomes. Readers could use their smartphones or other devices to scan images within print publications or digital content, triggering augmented reality (AR) experiences or accessing additional multimedia content. Empowering users through visual search resulted in the following achievements: 

• Enhanced content discovery: On average, people spend 13 minutes on interactive content, compared to 8.5 minutes on static content, and the implemented solution led to a significant increase in the time spent on the site. By integrating multimedia content such as videos, animations, or interactive elements, the company saw a boost in reader engagement, with users spending more time consuming and interacting with the content. 
• Differentiated content offerings: The innovative use of visual search and AR technology differentiated the company’s content offerings, attracting tech-savvy readers and setting it apart from competitors in the publishing industry. 

Training a computer to perceive, decode, and recognize visual information just like humans is no easy task. Developing an AI image recognition model requires tons of labeled and classified data. Custom computer vision software assists in training AI models by handling large volumes of labeled data, facilitating the computer’s ability to recognize visual information similar to humans. Let’s overview the visual search integration benefits. 

Benefits of Visual Search for Users 

When evaluating data visualization solutions, one should always keep the critical goal in mind: giving both decision-makers and users clear insight into the data. The technology’s common benefits include: 

• Improved search efficiency: B2B buyers often conduct extensive research before purchasing: an impressive 72% of US users regularly or always search for visual content before making a purchase. Object recognition in search allows them to find relevant information quickly and efficiently by using images to search for similar products or components. Properly selected visual search tools can streamline the research process and increase the likelihood that they will contact you for further inquiries. 
• Strengthening brand image and competence: 57% of clients won’t endorse businesses with badly designed mobile sites. Thus, underestimating user comfort automatically means losing customers, which competitors will not fail to take advantage of. Optimizing search results visually demonstrates professionalism and attention to detail and promotes the trust of potential partners in your brand. 
• Competitive advantage in E-commerce: Over 36% of shoppers utilize visual search, with more than half prioritizing visual over textual information when shopping online. As E-commerce continues to grow, implementing visual search can differentiate your online store from competitors who have not yet adopted this technology. This can attract new customers seeking a more visually appealing and informative buying experience. 

Additionally, optimizing images for visual search engines gives your website a higher chance of appearing in search results. Improving search accuracy with visuals can potentially drive more qualified traffic to your website and increase brand awareness among your target audience. 

Implementing Visual Search Technology  

Visual search software and tools can be integrated with a variety of existing platforms and built-in tools, including: 

1. E-commerce platforms (Shopify, WooCommerce, Magento, BigCommerce, Salesforce Commerce Cloud) 
2. Social media platforms (Instagram, TikTok, Pinterest, Facebook) 
3. Search engines (Google, Bing, Yahoo) 
4. Content management systems (CMS) (WordPress, Drupal, Joomla) 
5. Customer relationship management (CRM) systems (HubSpot, Salesforce) 
6. Cloud storage and hosting services (AWS, Google Cloud Platform, Microsoft Azure) 
7. Mobile operating systems (iOS, Android) 
8. Augmented reality (AR) platforms (ARKit, ARCore) 
9. Analytics and data visualization tools (Google Analytics, Adobe Analytics, Tableau) 

On the other hand, a customized visual search software integration will meet business needs in a company-specific way by tailoring the solution to align with the organization’s unique requirements, workflows, and objectives. As an AI software development provider, we prioritize the following three best practices for optimization and user experience: 

1. Continuous testing and iteration: 

• Regularly conduct usability testing and gather user feedback to identify improvement areas for visual search tools. 
• Utilize A/B testing to compare different versions of features or user interfaces and determine which performs better regarding user satisfaction. 
• Implement an agile development approach, allowing for frequent iterations and updates based on user feedback and changing market demands. 

2. Personalization and contextualization: 

• Leverage visual search applications to personalize user experiences by understanding individual preferences, behaviors, and context. 
• Provide tailored recommendations, content, or features based on user data, such as browsing history, purchase patterns, and demographic information. 
• Ensure the AI-based software adapts to users’ needs and preferences in real-time, enhancing user satisfaction and engagement. 

3. Seamless integration and scalability: 

• Design the visual search technology with scalability in mind, allowing it to handle increasing volumes of data and users as the business grows. 
• Ensure seamless integration with existing platforms, tools, and systems, making it easy for users to adopt and incorporate the AI software into their workflows. 
• Provide comprehensive documentation, APIs, and developer tools to facilitate integration with third-party applications and customizations according to users’ specific requirements. 

By following these practices, we match visual search technology benefits with ongoing business processes and deliver exceptional user experiences that drive business growth. 

Challenges in Visual Search 

Despite the advanced visual search capabilities, there are some challenges and limitations that need to be taken into account: 

• Training data: To produce accurate results, AI heavily relies on high-quality and diverse training data. The visual search algorithms should be constantly maintained and adjusted to produce relevant and meaningful data results. To address the challenge of training data, organizations can employ data augmentation techniques to increase the diversity and quantity of available data, ensuring the AI model learns from a broader range of examples. Collaboration with an experienced technological partner can also help gather labeled data more efficiently. 
• Input specificity: AI in image recognition may have difficulty recognizing objects that are not part of the training data set or have ambiguous features. Since in the real world it often happens that data fed to the system in the form of images is oriented in different directions, the system may produce inaccurate results due to these deviations. In other words, this means that, unlike humans, the system cannot understand that the orientation of an image (left, right, top, or bottom) makes no difference.  Addressing specificity challenges involves continually updating and expanding the AI model’s training data to include various objects and scenarios. Transfer learning techniques can also be utilized to adapt pre-trained models to recognize new objects with similar features, reducing the need for extensive new training data. 
• Ethical concerns: AI in image recognition can raise ethical concerns about privacy, security, and bias in decision-making processes. Organizations can mitigate ethical concerns by implementing strict data privacy and security measures, ensuring sensitive information is handled responsibly and transparently. Additionally, regular audits and bias detection algorithms can help identify and address biases in the AI model’s decision-making process, promoting fairness and accountability.

Conclusion  

The image recognition market is expected to hit $12.03 billion by 2024. This growth will have a widespread impact on businesses globally as they adopt image recognition technologies to improve operations and stay competitive. The key applications of visual recognition software across industries are ever-expanding and enable businesses to: 

• Automatically categorize products for inventory management and E-commerce 
• Detect objects and activities for refined search results 
• Filter inappropriate content on social media and online platforms 
• Interpret gestures and facial expressions for human-computer interaction 
• Analyze visual content for optimized marketing strategies and more 

The visual search AI software field is not at its peak yet so interesting developments can be expected in the next few years. Get ready to outperform your competitors with visual search innovations! Please contact us for a tailored solution that suits your unique business context, challenges, and objectives.