The ioMoVo AI engine automatically generates summarization and insights from facial images, tags, colors, categories, keywords, brands, objects, entities, sentiments, scenes, and audio or video captions. “One aspect of metadata we capture from facial recognition systems is that we identify and use faces as a form of search,” Hajeer commented. “This means identifying faces in videos or images, and we can search for a person by name in the digital library as well as for their face.” For color, ioMoVo can search for everything in the library that has been identified as blue, but it can also search for a particular color shade by entering the code for that and find all pictures with that shade.
“Users benefit from the same look and feel across all data sources,” commented Hajeer. “Our homegrown search engine can search all types of files, including relational databases, graph databases, and vector storage.” ioMoVo’s multimodal approach provides an integrated view of content. A taxonomy can be applied to the entire body of content regardless of format or where it is stored, with tags created either by AI or by users. ioMoVo aims to be an end-to-end digital media and content management solution that provides any services that are needed for optimizing use of digital content.
According to infotex, more than 300 million terabytes of data are created each day. As the volume of digital content increases, the ability to manage it becomes more important. Taxonomy and metadata are vital to finding products, conducting scientific research, and keeping track of organizational information. They also enable a wide variety of analytics on unstructured data. We can see the results of a well-designed taxonomy, but behind the scenes, there’s a lot more going on than meets the eye. Expect continued advances and steadily improving performance in taxonomy products and techniques.