Semantic interpretation of novelty in images using histograms of oriented gradients

Nicolas Alt, Werner Maier, Qing Rao, Eckehard Steinbach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An approach for the semantic interpretation of image-based novelty in real-world environments is presented. We measure novelty using the concept of pixel-based surprise, which quantifies how much a new observation changes the robot's current probabilistic appearance model of the environment. The corresponding surprise maps are utilized as prior information to reduce the search space of a "Histograms of Oriented Gradients" object detector. Specifically, detection windows are scored and selected using surprise values. Several object classes are simultaneously searched for and learned from a low number of manually taken reference images. Experiments are performed on a human-size robot in a cluttered household environment. Compared to object detection based on a search of the complete image, a 35-fold speed-up is observed. Additionally, the detection performance increases significantly.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications - 5th International Conference, ICIRA 2012, Proceedings
Pages427-436
Number of pages10
EditionPART 3
DOIs
StatePublished - 2012
Event5th International Conference on Intelligent Robotics and Applications, ICIRA 2012 - Montreal, QC, Canada
Duration: 3 Oct 20125 Oct 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7508 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Intelligent Robotics and Applications, ICIRA 2012
Country/TerritoryCanada
CityMontreal, QC
Period3/10/125/10/12

Keywords

  • Novelty detection
  • Object class detection
  • Visual attention

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