Crawling, indexing, and retrieving moments in videogames
Xiaoxuan Zhang, Zeping Zhan, Misha Holtz, Adam M. Smith · 2018
We introduce the problem of content-based retrieval for moments in videogames. This new area for artificial intelligence in games exercises automated gameplay and visual understanding while making connections to information retrieval. We propose a number of techniques to discover the interesting moments in a game (crawling), show how to compress moments into an efficiently searchable structure (indexing), and recall those moments most relevant to a user-provided query (retrieving). We combine these ideas in a prototype visual search engine and compare it with commercial visual search engines. Searching within a corpus of moments from Super Nintendo Entertainment System games using query images extracted from YouTube videos, our prototype is able to identify moments that web-oriented search engines rarely see.