Reliable attribute-based object recognition using high predictive value classifiers

Wentao Luan, Yezhou Yang, Cornelia Fermüller, John S. Baras

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


We consider the problem of object recognition in 3D using an ensemble of attribute-based classifiers. We propose two new concepts to improve classification in practical situations, and show their implementation in an approach implemented for recognition from point-cloud data. First, the viewing conditions can have a strong influence on classification performance. We study the impact of the distance between the camera and the object and propose an approach to fusing multiple attribute classifiers, which incorporates distance into the decision making. Second, lack of representative training samples often makes it difficult to learn the optimal threshold value for best positive and negative detection rate. We address this issue, by setting in our attribute classifiers instead of just one threshold value, two threshold values to distinguish a positive, a negative and an uncertainty class, and we prove the theoretical correctness of this approach. Empirical studies demonstrate the effectiveness and feasibility of the proposed concepts.

Original languageEnglish (US)
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319464862
StatePublished - 2016
Externally publishedYes
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: Oct 8 2016Oct 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9907 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th European Conference on Computer Vision, ECCV 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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