Search & Similarity API
With the plethora of images and videos that firms are tasked with managing, the simple act of maintaining a searchable, organized library of all asset types is impossible without an automated and scalable solution. With appropriate search functions, advertisers and publishers alike can build well organized, searchable library that can be monetized
Netra’s Search and Similarity allows for an immediate search of all asset inventory to return similar assets, allowing to enormous efficiency gains and a more effective asset management strategy.
Why our API is game-changing:
Unlike other offerings which rely on text and tagging, Netra’s technology scans and determines similarity based on the content embedded within a video.
- This service allows for the discovery of specific keywords, phrases or filters based upon content titles, descriptions, tags, and the Netra metadata about the image content.
- Searches can be customized to specific types of data and specific date ranges.
- Search results can be deployed to other systems such as ad servers for trafficing.
- This service provides a way to discover similar content to one or more content items.
- The query can be any combination of images, videos, or documents and the resulting output provides a powerful way of locating relevant content.
- This solution provides an easy, scalable, and intuitive way of organizing content based upon Netra-generated metadata rather than manual content tagging and taxonomy alignments.
- Search Benefits include:
- Fast and scalable full-text search
- Quickly analyze unstructured and semi-structured data to empower content understanding and relationships and build out more scale for audience buying
Additional technical considerations and features:
- We use a RESTful API that conforms to the constraints of REST architectural style and allow for interaction with RESTful web services.
- We leverage an Approximate Nearest Neighbor algorithm to facilitate the association of analyzed results to the content in question.
- The results provide a priority stacked list of correlated content based upon the vectorization of the content Netra has processed which is organized by descending similarity.