Ferret: A Toolkit for Content-Based Similarity Search of Feature-Rich Data

Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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, multi-feature 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 several other components to help system builders address search system infrastructure issues. We have implemented the toolkit and used it to successfully construct content-based similarity search systems for four data types: Audio recordings, digital photos, 3D shape models and genomic microarray data.

Original languageEnglish (US)
Pages (from-to)317-330
Number of pages14
JournalOperating Systems Review (ACM)
Volume40
Issue number4
DOIs
StatePublished - Oct 2006

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

Keywords

  • feature-rich data
  • similarity search
  • sketch
  • toolkit

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