Ferret: a toolkit for content-based similarity search of feature-rich data

Building content-based search tools for feature-rich data has been a challenging problem because feature-rich data such as audio recordings, digital images, and sensor data are inherently noisy and high dimensional. Comparing noisy data requires comparisons based on similarity instead of exact matches, and thus searching for noisy data requires similarity search instead of exact search. The Ferret toolkit is designed to help system builders quickly construct content-based similarity search systems for feature-rich data types. The key component of the toolkit is a content-based similarity search engine for generic, multifeature object representations. To solve the similarity search problem in high-dimensional spaces, we have developed approximation methods inspired by recent theoretical results on dimension reduction. The search engine constructs sketches from feature vectors as highly compact data structures for matching, filtering and ranking data objects. The toolkit also includes ...

• Type: Conference
• Year: 2006
• Where: EUROSYS
• Authors: Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li
• Keywords: Content-based Similarity Search, EUROSYS 2006, Similarity Search Problem, Similarity Search Systems, System Software