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2016 | OriginalPaper | Chapter

Harvesting Training Images for Fine-Grained Object Categories Using Visual Descriptions

Authors : Josiah Wang, Katja Markert, Mark Everingham

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

We harvest training images for visual object recognition by casting it as an IR task. In contrast to previous work, we concentrate on fine-grained object categories, such as the large number of particular animal subspecies, for which manual annotation is expensive. We use ‘visual descriptions’ from nature guides as a novel augmentation to the well-known use of category names. We use these descriptions in both the query process to find potential category images as well as in image reranking where an image is more highly ranked if web page text surrounding it is similar to the visual descriptions. We show the potential of this method when harvesting images for 10 butterfly categories: when compared to a method that relies on the category name only, using visual descriptions improves precision for many categories.

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Footnotes
1
Previous work [1, 5, 15] has used visual descriptions for object recognition without any training images but not for the discovery of training images itself.
 
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Metadata
Title
Harvesting Training Images for Fine-Grained Object Categories Using Visual Descriptions
Authors
Josiah Wang
Katja Markert
Mark Everingham
Copyright Year
2016
Publisher
Springer International Publishing
Book
Advances in Information Retrieval

Print ISBN: 978-3-319-30670-4

Electronic ISBN: 978-3-319-30671-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-30671-1_40