What types of usage does Watson Visual Recognition cater to?

Watson Visual Recognition is designed to analyze and interpret visual content in various forms. It caters to multiple use cases, including object detection, scene analysis, and facial recognition. Object detection allows the model to identify and locate specific objects within an image, which can be crucial for applications such as surveillance or inventory management. Scene analysis enables the system to understand the broader context of an image, helping in categorization and management of photographs based on their content. Facial recognition is another critical capability that allows for identifying and verifying individuals within images, enhancing personalization and security across different applications.

The other choices mention functionalities that fall outside the primary focus of Watson Visual Recognition. Clinical analysis of images pertains more to medical imaging technologies rather than general visual recognition capabilities. Image storage and encryption relate to data management rather than content interpretation, which is not the purpose of Watson Visual Recognition. Text extraction from documents is typically associated with Optical Character Recognition (OCR) technologies, rather than visual recognition of images. Thus, the choice highlighting object detection, scene analysis, and facial recognition accurately reflects the core functionalities of Watson Visual Recognition.