The state of the art: object retrieval in paintings using discriminative regions

The objective of this work is to recognize object categories (such as animals and vehicles) in paintings, whilst learning these categories from natural images. This is a challenging problem given the substantial differences between paintings and natural images, and variations in depiction of objects...

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Main Authors: Crowley, E, Zisserman, A
Format: Conference item
Published: British Machine Vision Association 2014
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author Crowley, E
Zisserman, A
author_facet Crowley, E
Zisserman, A
author_sort Crowley, E
collection OXFORD
description The objective of this work is to recognize object categories (such as animals and vehicles) in paintings, whilst learning these categories from natural images. This is a challenging problem given the substantial differences between paintings and natural images, and variations in depiction of objects in paintings. We first demonstrate that classifiers trained on natural images of an object category have some success in retrieving paintings containing that category. We then draw upon recent work in mid-level discriminative patches to develop a novel method for re-ranking paintings based on their spatial consistency with natural images of an object category. This method combines both class based and instance based retrieval in a single framework. We quantitatively evaluate the method over a number of classes from the PASCAL VOC dataset, and demonstrate significant improvements in rankings of the retrieved paintings over a variety of object categories.
first_indexed 2024-03-06T22:49:54Z
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spelling oxford-uuid:5e6a77cc-28a5-4ebc-b6a2-b2de4612316f2022-03-26T17:40:44ZThe state of the art: object retrieval in paintings using discriminative regionsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:5e6a77cc-28a5-4ebc-b6a2-b2de4612316fSymplectic Elements at OxfordBritish Machine Vision Association2014Crowley, EZisserman, AThe objective of this work is to recognize object categories (such as animals and vehicles) in paintings, whilst learning these categories from natural images. This is a challenging problem given the substantial differences between paintings and natural images, and variations in depiction of objects in paintings. We first demonstrate that classifiers trained on natural images of an object category have some success in retrieving paintings containing that category. We then draw upon recent work in mid-level discriminative patches to develop a novel method for re-ranking paintings based on their spatial consistency with natural images of an object category. This method combines both class based and instance based retrieval in a single framework. We quantitatively evaluate the method over a number of classes from the PASCAL VOC dataset, and demonstrate significant improvements in rankings of the retrieved paintings over a variety of object categories.
spellingShingle Crowley, E
Zisserman, A
The state of the art: object retrieval in paintings using discriminative regions
title The state of the art: object retrieval in paintings using discriminative regions
title_full The state of the art: object retrieval in paintings using discriminative regions
title_fullStr The state of the art: object retrieval in paintings using discriminative regions
title_full_unstemmed The state of the art: object retrieval in paintings using discriminative regions
title_short The state of the art: object retrieval in paintings using discriminative regions
title_sort state of the art object retrieval in paintings using discriminative regions
work_keys_str_mv AT crowleye thestateoftheartobjectretrievalinpaintingsusingdiscriminativeregions
AT zissermana thestateoftheartobjectretrievalinpaintingsusingdiscriminativeregions
AT crowleye stateoftheartobjectretrievalinpaintingsusingdiscriminativeregions
AT zissermana stateoftheartobjectretrievalinpaintingsusingdiscriminativeregions