Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval

Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually...

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Main Authors: Rongrong Ji, Xing Xie, Hongxun Yao, Wei-ying Ma, Heilongjiang P. R
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
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DML
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.6387
http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf
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spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.180.6387 2023-05-15T16:01:59+02:00 Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval Rongrong Ji Xing Xie Hongxun Yao Wei-ying Ma Heilongjiang P. R The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.6387 http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.6387 http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf text ftciteseerx 2016-01-07T16:27:34Z Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of “visual words"; (2) the model cannot adapt to database variance. In this paper, we describe an unsupervised optimization strategy in generating the hierarchy structure of visual vocabulary, which produces a more effective and adaptive retrieval model for large-scale search. We adopt a novel Density-based Metric Learning (DML) algorithm, which corrects word quantization bias without supervision in hierarchy optimization, based on which we present a hierarchical rejection chain for efficient online search based on the vocabulary hierarchy. We also discovered that by hierarchy optimization, efficient and effective transfer of a retrieval model across different databases is feasible. We deployed a large-scale image retrieval system using a vocabulary tree model to validate our advances. Experiments on UKBench and street-side urban scene databases demonstrated the effectiveness of our hierarchy optimization approach in comparison with state-of-the-art methods. 1. Text DML Unknown
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description Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of “visual words"; (2) the model cannot adapt to database variance. In this paper, we describe an unsupervised optimization strategy in generating the hierarchy structure of visual vocabulary, which produces a more effective and adaptive retrieval model for large-scale search. We adopt a novel Density-based Metric Learning (DML) algorithm, which corrects word quantization bias without supervision in hierarchy optimization, based on which we present a hierarchical rejection chain for efficient online search based on the vocabulary hierarchy. We also discovered that by hierarchy optimization, efficient and effective transfer of a retrieval model across different databases is feasible. We deployed a large-scale image retrieval system using a vocabulary tree model to validate our advances. Experiments on UKBench and street-side urban scene databases demonstrated the effectiveness of our hierarchy optimization approach in comparison with state-of-the-art methods. 1.
author2 The Pennsylvania State University CiteSeerX Archives
format Text
author Rongrong Ji
Xing Xie
Hongxun Yao
Wei-ying Ma
Heilongjiang P. R
spellingShingle Rongrong Ji
Xing Xie
Hongxun Yao
Wei-ying Ma
Heilongjiang P. R
Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval
author_facet Rongrong Ji
Xing Xie
Hongxun Yao
Wei-ying Ma
Heilongjiang P. R
author_sort Rongrong Ji
title Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval
title_short Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval
title_full Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval
title_fullStr Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval
title_full_unstemmed Vocabulary Hierarchy Optimization for Effective and Transferable Retrieval
title_sort vocabulary hierarchy optimization for effective and transferable retrieval
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.6387
http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf
genre DML
genre_facet DML
op_source http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.6387
http://vilab.hit.edu.cn/%7Errji/index_files/1184.pdf
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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