Learning to Identify Local Flora with Human Feedback (Author's Manuscript)

Abstract

In this ongoing work, we are developing a method that involves a user in the loop to aid in the fine-grained recognition of a diverse set of tree species. Instead of asking users to provide attributes of trees, we instead ask them to judge the similarity between pairs of tree images, and then use this to learn the parameters of a discriminative distance metric for use with k-nearest neighbors. Over time, the discriminative distance function becomes a better approximation to the humans judgment of visual similarity. We present baselines and results of our human-guided approach on a collection of 20 tree species from five geographic locations.

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Document Details

Document Type
Technical Report
Publication Date
Jun 23, 2014
Accession Number
AD1039800

Entities

People

  • David Crandall
  • Stefan Lee

Organizations

  • Indiana University Bloomington

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Algorithms
  • Classification
  • Computer Vision
  • Computers
  • Distance Learning
  • Feature Extraction
  • Governments
  • Identification
  • Language
  • Learning
  • Military Research
  • Recognition
  • Test Sets
  • Training

Fields of Study

  • Computer science

Readers

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  • Computer Vision.
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