SuperLabel: A Superpixel Labeling Interface for Semantic Image Annotation

Abstract

Manual image annotation is a tedious, yet necessary, task to collect labels that can be used for supervised machine learning algorithms. Most existing interfaces used to assign labels to pixels in an image require the user to hand outline each distinct object or visual concept in the image. This task can be quite time consuming, especially for objects of non-rectangular shape (e.g., trees or people). This report introduces a new annotation interface called SuperLabel that makes use of super pixel segmentation to automatically define object regions so the user simply has to assign a label to these regions instead of also manually defining them. The report provides details on how to set up and launch SuperLabel, and describes the labeling functionality provided by the system to label sets of images.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2018
Accession Number
AD1060572

Entities

People

  • Maggie Wigness

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Boundaries
  • Computer Vision
  • Demographic Cohorts
  • Directories
  • Graphical User Interface
  • Information Science
  • Learning
  • Machine Learning
  • Military Research
  • Natural Languages
  • Supervised Machine Learning
  • Two Dimensional
  • User Interface

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks