VideoDB Documentation

Building World's First Video Database

Why do we need a Video Database Now?

When we started building

VideoDB⁠

, we had to answer the question of Why we need a Video Database Now? Video content is exploding on the internet. Cisco predicts that 82% of all consumer internet traffic will be video by 2022. Platforms like YouTube, Netflix and Facebook are streaming endless hours of video to users daily. Businesses are incorporating rich visual media into marketing campaigns. Video conferencing has become vital for remote work. Clearly, we are in a video commerce era.

However, all this video creates massive headaches around search, management and utilization. Platforms rely on messy text matching or ineffective manual tagging to organize content. Users struggle to find relevant clips in massive archives. Videos disappear into black holes on company networks never to be seen again. There has to be a better way to control this video data deluge. Databases have long helped tame massive collections of business data, customer records and financial transactions. We need to now apply database techniques to harness ever-growing video repositories. Specifically, we need video database management systems (VDBMS) with the following capabilities: Metadata Extraction Raw video files lack innate structure. Video databases need to automatically parse clips and extract descriptive features and metadata to make them searchable. This involves processing methods like segmentation, keyframe extraction and visual attribute analysis. Cataloging & Indexing The extracted metadata then needs to be indexed with tags and markers that annotate video content at different granularities - whole clip, segments, objects, etc. This descriptor metadata serves as the basis for search, just like keywords or columns in conventional databases. Storage Optimization Intelligent compression and streaming allow video databases to efficiently store and deliver very large files. Optimization for bandwidth, quality and client display constraints is handled automatically unlike basic network folders. Similarity Search The defining feature of multimedia databases is content-based retrieval. Users search based on parameters and examples, not just text matching. Video DBMS handle complex queries across visual descriptors and return results ranked by robust similarity metrics. Additionally, multimodal models that can understand and generate language, images, video, speech, etc. in an integrated way are gaining steam. They overcome limitations of text-only systems for applications like video captioning, perceptual grounding and human-computer interaction. Unified multimodal foundations for general intelligence are evolving quickly.

The future is exciting for the next generation of internet 🙌🏼

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