VisFM: Visual Analysis of Image Feature Matchings

Chenhui Li, George Baciu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Feature matching is the most basic and pervasive problem in computer vision and it has become a primary component in big data analytics. Many tools have been developed for extracting and matching features in video streams and image frames. However, one of the most basic tools, that is, a tool for simply visualizing matched features for the comparison and evaluation of computer vision algorithms is not generally available, especially when dealing with a large number of matching lines. We introduce VisFM, an integrated visual analysis system for comprehending and exploring image feature matchings. VisFM presents a matching view with an intuitive line bundling to provide useful insights regarding the quality of matched features. VisFM is capable of showing a summarization of the features and matchings through group view to assist domain experts in observing the feature matching patterns from multiple perspectives. VisFM incorporates a series of interactions for exploring the feature data. We demonstrate the visual efficacy of VisFM by applying it to three scenarios. An informal expert feedback, conducted by our collaborator in computer vision, demonstrates how VisFM can be used for comparing and analysing feature matchings when the goal is to improve an image retrieval algorithm.
Original languageEnglish
Article numberhttps://doi.org/10.1111/cgf.13391
Pages (from-to)45-58
JournalComputer Graphics Forum
Volume38
Issue number1
DOIs
Publication statusPublished - 1 Feb 2019

Keywords

  • Image processing
  • Information visualization
  • Visual analytics

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design

Cite this