Three things everyone should know to improve object retrieval

The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28]. We make the following three contributions: (i) a new method to compare SIFT descriptors (Roo...

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Main Authors: Arandjelović, R, Zisserman, A
Format: Conference item
Language:English
Published: IEEE 2012
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author Arandjelović, R
Zisserman, A
author_facet Arandjelović, R
Zisserman, A
author_sort Arandjelović, R
collection OXFORD
description The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28]. We make the following three contributions: (i) a new method to compare SIFT descriptors (RootSIFT) which yields superior performance without increasing processing or storage requirements; (ii) a novel method for query expansion where a richer model for the query is learnt discriminatively in a form suited to immediate retrieval through efficient use of the inverted index; (iii) an improvement of the image augmentation method proposed by Turcot and Lowe [29], where only the augmenting features which are spatially consistent with the augmented image are kept. We evaluate these three methods over a number of standard benchmark datasets (Oxford Buildings 5k and 105k, and Paris 6k) and demonstrate substantial improvements in retrieval performance whilst maintaining immediate retrieval speeds. Combining these complementary methods achieves a new state-of-the-art performance on these datasets.
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spelling oxford-uuid:423bf16b-4ac6-4e41-bf34-bb7c20ba1b992024-12-17T15:05:09ZThree things everyone should know to improve object retrievalConference itemhttp://purl.org/coar/resource_type/c_5794uuid:423bf16b-4ac6-4e41-bf34-bb7c20ba1b99EnglishSymplectic ElementsIEEE2012 Arandjelović, RZisserman, AThe objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28]. We make the following three contributions: (i) a new method to compare SIFT descriptors (RootSIFT) which yields superior performance without increasing processing or storage requirements; (ii) a novel method for query expansion where a richer model for the query is learnt discriminatively in a form suited to immediate retrieval through efficient use of the inverted index; (iii) an improvement of the image augmentation method proposed by Turcot and Lowe [29], where only the augmenting features which are spatially consistent with the augmented image are kept. We evaluate these three methods over a number of standard benchmark datasets (Oxford Buildings 5k and 105k, and Paris 6k) and demonstrate substantial improvements in retrieval performance whilst maintaining immediate retrieval speeds. Combining these complementary methods achieves a new state-of-the-art performance on these datasets.
spellingShingle Arandjelović, R
Zisserman, A
Three things everyone should know to improve object retrieval
title Three things everyone should know to improve object retrieval
title_full Three things everyone should know to improve object retrieval
title_fullStr Three things everyone should know to improve object retrieval
title_full_unstemmed Three things everyone should know to improve object retrieval
title_short Three things everyone should know to improve object retrieval
title_sort three things everyone should know to improve object retrieval
work_keys_str_mv AT arandjelovicr threethingseveryoneshouldknowtoimproveobjectretrieval
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