Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2015

Abstract

The conventional bag-of-visual-words (BoW) model is popular for the large-scale object retrieval system but suffers from the critical drawback of ignoring spatial information. RANSAC-based methods attempt to remedy this drawback, but often require traversing all the feature matches for each hypothesis, leading to the heavy computational cost which limits the number of gallery images to be verified for each online query. We propose an efficient direct spatial matching (DSM) approach to directly estimate the scale variation using region sizes, in which all feature matches voted for estimating geometric transformation. DSM is much faster than RANSAC-based methods and exhaustive enumeration approaches. A logarithmic term frequency-inverse document frequency (log tf-idf) weighting scheme is introduced to boost the performance of the base system. We have conducted extensive experimental evaluations on four benchmark datasets for object retrieval. The proposed DSM method, together with a carefully-tailored reranking scheme, achieves the state-of-the-art results on the Oxford buildings and Paris datasets, which demonstrates the efficacy and scalability of our novel DSM technique for large scale object retrieval systems.

Keywords

Images reranking, log tf-idf, object retrieval, spatial matching

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Multimedia

Volume

17

Issue

8

First Page

1391

Last Page

1397

ISSN

1520-9210

Identifier

10.1109/TMM.2015.2446201

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional URL

https://doi.org/10.1109/TMM.2015.2446201

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