Reverse Image Search on Social at Scale

Visual content has emerged as a powerful medium of communication on social media, courtesy its ability to transcend linguistic boundaries and convey messages instantaneously, Today, billions of images are shared across social media platforms daily and it’s vital that brands and organizations possess the capability to monitor and analyze it accurately.However, this surge in visual content also presents unique challenges for businesses reliant on traditional text-based analytics for social media monitoring. Let’s elaborate.

Traditionally, brands have used text-based analytics tools to assess online presence. However, the growing volume of visual content exchanging hands on social media necessitates a shift towards sophisticated solutions capable of analyzing comprehensive visual content at scale while protecting intellectual property.  

Recognizing the critical need for advanced tools to navigate this complex landscape, Sprinklr is at the forefront of a technological revolution with its state-of-the-art Reverse Image Search technology. 

Bridging the analytics gap with vector embeddings 

In the realm of artificial intelligence and machine learning, vector embeddings play a pivotal role in enabling computers to interpret and process the complex, unstructured data that make up human language and visual content. At the core of Sprinklr's Reverse Image Search technology lies the sophisticated application of these vector embeddings, a method that has revolutionized the way we understand and categorize images and text within the same contextual framework.

Vector embeddings are high-dimensional vectors used to represent data points, such as words, sentences or images, in a continuous vector space. Each dimension of the vector captures some aspect of the data's meaning or context, allowing seemingly disparate forms of content to be compared and analyzed based on their semantic and contextual similarities. This representation is crucial for processing and understanding the vast and varied data encountered in social media. 

In the context of Sprinklr's technology, vector embeddings enable the mapping of images and their corresponding text captions into a shared vector space. This is achieved through a model trained using contrastive loss, a technique that encourages the model to learn which images and texts are similar (or related) and which are not. By doing so, the model becomes adept at identifying and understanding the nuanced visual concepts conveyed through natural language supervision.

The process: From image upload to categorization 

Sprinklr's Reverse Image Search technology simplifies the process of managing images from the moment they're uploaded to when they're categorized, which is crucial for brands tracking their visual content on social media. This technology is equipped to handle various challenges, such as:  

• Detecting unauthorized image use 
• Tracking brand visibility 
• Gauging consumer sentiment through images 

It offers a thorough solution for analyzing visual content with remarkable accuracy and speed. The process starts when an image is uploaded to Sprinklr, setting off a chain of advanced procedures to decode the image's information. Here’s how the process goes: 

Step 1: The initial processing checks the image's quality and dimensions, ensuring it's ready for the next step of vector embedding generation. This foundational step is vital for the subsequent, in-depth image analysis.  

Step 2: The core of Sprinklr's technology lies in converting the image into a vector embedding — a high-dimensional vector that encapsulates the image's key features and context. This conversion is done using a model trained to understand images in relation to their captions, which allows for the comparison of visual content within a shared vector space. This encoding is essential for placing the image within the larger context of social media content. 

Step 3: After creating the vector embedding, it's stored in a specialized vector index, an optimized database built to manage the immense volume of vector embeddings produced by the platform. This index is crucial for the quick retrieval and comparison of embeddings, allowing the platform to handle the scale of social media image analysis. 

Step 4: Finally, the platform can categorize new images from social media by generating their vector embeddings and conducting an approximate nearest neighbor (ANN) search against the vector index. This search finds the most contextually similar images, enabling swift categorization. The ANN search results help categorize images into exact, highly similar, or low similar matches, providing users with detailed insights into their visual content's social media usage, brand impact and intellectual property considerations.    

Meeting the challenges of scale 

The exponential growth of social media has led to an unprecedented surge in visual content, presenting significant challenges in monitoring and analyzing images at scale. Sprinklr's Reverse Image Search technology is specifically designed to address these challenges, ensuring efficient and accurate processing of vast quantities of images.  

Challenge 1: High computational intensity

One of the primary challenges in processing images at scale is the computational intensity required to generate vector embeddings for every image. Given the sheer volume of images shared on social media platforms daily, this task demands a highly efficient and scalable solution.  

Sprinklr’s solution: To tackle this challenge, Sprinklr leverages shared computation resources by integrating our Reverse Image Search technology with an existing visual engine. This visual engine, which already operates at a social media scale, shares the same backbone model as our vector embedding model. By utilizing shared computation, we significantly reduce the computational overhead, enabling the rapid and cost-effective generation of vector embeddings. This approach not only enhances process efficiency but also ensures that our technology can scale to meet the growing demands of social media analytics.  

Challenge 2: High search request volume

The second major challenge in processing images at scale is efficiently running the vector search service to handle the high volume of search requests. This requires a search solution that is not only fast and accurate but also capable of scaling with the increasing volume of query images.  

Sprinklr’s solution: Sprinklr addresses this challenge by employing Elastic Search, a powerful search and analytics engine known for its scalability and performance. With the introduction of version 8 and above, Elastic Search has added support for approximate nearest neighbor (ANN) search and vector type fields. These features are integral to our Reverse Image Search technology. By creating indexes directly on Elastic Search, we leverage its ability to handle the load of social media scale content efficiently. This ensures that our vector search service can quickly and accurately process search requests, providing users with timely and relevant results.  

Furthermore, Elastic Search's scalable architecture, which stores indexes on disk rather than in memory, allows us to accommodate the growing number of query images without the need for proportional increases in memory resources. Currently, we handle around 100 million queries every day while maintaining a latency of less than 150ms.   

Challenge 3: Complex embeddings search

Another challenge is searching for text embeddings in the same vector space as image embeddings.  

Sprinklr’s solution: Sprinklr overcomes this challenge of scale with their technology's multimodal capability. This approach not only enhances the accuracy and relevance of our search results but also provides a more comprehensive analysis of social media content. By using the same index for both text-based and image-based searches, we can streamline the search process and reduce the computational resources required for separate analyses. This multimodal approach allows us to efficiently process and analyze the vast and diverse content found on social media platforms, offering users a holistic view of their brand's presence and engagement.  

Specific use cases on how brands leverage Sprinklr’s reverse image search  

• Identify images that gained the most engagement to optimize your campaign strategy. Compare the performance of assets against each other to determine the best performing images.  
• Accurately measure campaign ROI by capturing engagements for an image asset shared in multiple messages/posts across social sources.  
• Ensure the safety of your copyrighted images by identifying instances of them being distributed online without your approval. In cases where leaks occur, swiftly determine the origin and take appropriate action. 

Sprinklr – Committed to serving customers by helming innovation & technology

Meeting the challenges of scale in social media analytics requires innovative solutions and strategic approaches. Sprinklr's Reverse Image Search technology, with its efficient calculation of vector embeddings, scalable vector search with Elastic Search and multimodal capabilities, represents a significant advancement in the field.  

By addressing the computational and scalability challenges head-on, we ensure that our technology remains at the forefront of social media analytics, empowering brands and organizations to effectively monitor and analyze visual content at unbelievable scales. This technology not only enhances brand protection but also opens up new opportunities for engagement and insight into the visual language of social media. As we continue to refine and expand our capabilities, Sprinklr remains committed to providing our clients with the most advanced tools for navigating the complex and dynamic world of social media.