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All vehicles are cars: subclass preferences in container concepts

Published: 05 June 2012 Publication History

Abstract

This paper investigates the natural bias humans display when labeling images with a container label like vehicle or carnivore. Using three container concepts as subtree root nodes, and all available concepts between these roots and the images from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset, we analyze the differences between the images labeled at these varying levels of abstraction and the union of their constituting leaf nodes. We find that for many container concepts, a strong preference for one or a few different constituting leaf nodes occurs. These results indicate that care is needed when using hierarchical knowledge in image classification: if the aim is to classify vehicles the way humans do, then cars and buses may be the only correct results.

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Cited By

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  • (2016)The ImageNet ShuffleProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912036(175-182)Online publication date: 6-Jun-2016
  • (2016)Pooling Objects for Recognizing Scenes without ExamplesProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912007(143-150)Online publication date: 6-Jun-2016
  • (2016)Socializing the Semantic GapACM Computing Surveys10.1145/290615249:1(1-39)Online publication date: 6-Jun-2016
  • Show More Cited By

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cover image ACM Conferences
ICMR '12: Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
June 2012
489 pages
ISBN:9781450313292
DOI:10.1145/2324796
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 June 2012

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Author Tags

  1. classifier combination
  2. hierarchical image recognition
  3. large scale image recognition

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ICMR '12 Paper Acceptance Rate 50 of 145 submissions, 34%;
Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

View all
  • (2016)The ImageNet ShuffleProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912036(175-182)Online publication date: 6-Jun-2016
  • (2016)Pooling Objects for Recognizing Scenes without ExamplesProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912007(143-150)Online publication date: 6-Jun-2016
  • (2016)Socializing the Semantic GapACM Computing Surveys10.1145/290615249:1(1-39)Online publication date: 6-Jun-2016
  • (2013)Improving tag-based image search by using linked open dataProceedings of the 10th Conference on Open Research Areas in Information Retrieval10.5555/2491748.2491753(21-24)Online publication date: 15-May-2013

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