Visual Search Enhancement

Search isn’t about typing anymore. It’s about snapping. 📸 Visual search is transforming how people discover products online. Google Lens handles 12 billion searches per month, and Pinterest Lens sees 600 million. Younger generations, especially Gen Z, prefer visual-first discovery over text. Smartphone cameras + AI = instant product recognition. Visual search drives faster discovery, better decisions, and higher confidence. Google Lens and Pinterest Lens are shaping the future of SEO beyond keywords. Brands with weak visuals or missing structured data risk being invisible. Optimized images, alt text, and product context can boost e-commerce discovery 30%+. Retailers using visual search see 48% faster discovery and 25% higher conversion rates. By 2028, 50% of searches will be visual or voice-driven. Early optimization means brands dominate tomorrow’s AI-powered shopping landscape. Are your images ready for the future of search?

In today's data-driven world, organizations are sitting on a treasure trove of information. But what good is all that data if you can't find what you need? That's where multimodal search comes in. Multimodal search is a new technology that allows you to search for information using images and videos. This can be a game-changer for organizations that have a lot of visual content, such as retail, media, and healthcare. 𝐇𝐞𝐫𝐞'𝐬 𝐚 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨: Imagine you're a retail company with a large library of product images. A customer comes to you with a picture of a product they want to buy, but they don't know the name of the product. With multimodal search, you can easily find the product in your inventory, even if it's not labeled correctly. 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤? Multimodal search uses a combination of natural language processing (NLP), BigQuery, and embeddings to create a system that can understand the meaning of images and videos. 👉 Embeddings: The system first creates numerical representations of the content, called embeddings. These embeddings capture the essential features of the image or video, such as the colors, shapes, and objects that appear in it. 👉 Vector Index: Then, a vector index is created to allow for efficient searching. The vector index is like a giant dictionary that maps embeddings to the images and videos that they represent. 👉 Query Embeddings: Finally, the user's query is turned into an embedding and compared to the indexed embeddings to find similar images and videos. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐟𝐨𝐫 𝐎𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬: 👉 Improved Search Accuracy: Multimodal search can help organizations find the information they need more quickly and accurately. This can lead to increased productivity and efficiency. 👉 Enhanced Customer Experience: Multimodal search can provide customers with a more intuitive and engaging way to find the products they're looking for. This can lead to increased customer satisfaction and loyalty. 👉 New Insights from Data: Multimodal search can help organizations unlock new insights from their data. For example, you can use multimodal search to identify trends in customer behavior or to discover new product opportunities. 𝐌𝐨𝐫𝐞 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐡𝐞𝐫𝐞: https://lnkd.in/dVwhDJkH If you're interested in learning more about multimodal search, check out the blog post below. It provides a step-by-step guide on how to implement a similar solution. Follow Omkar Sawant for more! #multimodalsearch #nlp #bigquery #embeddings #dataanalytics #bigdata #innovation #datascience #machinelearning #artificialintelligence

Google introduced ImageInWords, a new AI technology described in the recently published paper that could make a noticeable difference in how we handle visual data. Imagine searching for images online and getting results that really match what you're looking for. ImageInWords aims to make image search more accurate by understanding the content and context of images better by Unlocking Hyper-Detailed Image Descriptions. This could mean finding exactly the product, place, or concept you have in mind without wading through unrelated results. Beyond search, ImageInWords is also set to improve text-to-image AI. This means creating images from descriptions could become more accurate and relevant. For those in creative fields, this could help turn ideas into visuals more effectively. For businesses, it might mean better marketing visuals that align closely with product descriptions or promotional content. The potential uses for ImageInWords are broad, from enhancing online shopping experiences to supporting creative projects. It's a step forward in making our interactions with visual data a bit more intuitive and efficient. Link to the paper: https://lnkd.in/d5n3KCgr #AI #MachineLearning #ImageSearch #TextToImage

Text-based search ensures relevance but can lack diversity, especially when similar items share textual descriptions. The "Retrieve Results with Text; Diversify Them with Images" approach addresses this by adding a visual reranking step. After initial text-based retrieval, image embeddings are used for clustering (e.g., k-means), diversifying the results by prioritizing visually distinct items. This maintains relevance while offering users a broader selection. The added computational cost of clustering is a trade-off, but the improved diversity is often worth it, particularly in visually oriented domains like fashion or product search. https://lnkd.in/gAbDZyp2