Active Scene Classification via Dynamically Learning Prototypical Views

Zachary A. Daniels, Dimitris N. Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Scene classification is an important computer vision problem with applications to a wide range of domains including remote sensing, robotics, autonomous driving, defense, and surveillance. However, many approaches to scene classification make simplifying assumptions about the data, and many algorithms for scene classification are ill-suited for real-world use cases. Specifically, scene classification algorithms generally assume that the input data consists of single views that are extremely representative of a limited set of known scene categories. In real-world applications, such perfect data is rarely encountered. In this paper, we propose an approach for active scene classification where an agent must assign a label to the scene with high confidence while minimizing the number of sensor adjustments, and the agent is also embedded with the capability to dynamically update its underlying machine learning models. Specifically, we employ the Dynamic Data-Driven Applications Systems paradigm: our machine learning model drives the sensor manipulation, and the data captured by the manipulated sensor is used to update the machine learning model in a feedback control loop. Our approach is based on learning to identify prototypical views of scenes in a streaming setting.

Original languageEnglish (US)
Title of host publicationDynamic Data Driven Application Systems - Third International Conference, DDDAS 2020, Proceedings
EditorsFrederica Darema, Erik Blasch, Sai Ravela, Alex Aved
PublisherSpringer Science and Business Media Deutschland GmbH
Pages179-187
Number of pages9
ISBN (Print)9783030617240
DOIs
StatePublished - 2020
Event3rd International Conference on Dynamic Data Driven Application Systems, DDDAS 2020 - Boston, United States
Duration: Oct 2 2020Oct 4 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12312 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Dynamic Data Driven Application Systems, DDDAS 2020
Country/TerritoryUnited States
CityBoston
Period10/2/2010/4/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Active learning
  • Active vision
  • Computer vision
  • Dynamic data driven applications systems
  • Prototype learning
  • Scene classification

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