Article

Image similarity search with compact data structures

Authors:

Moses Charikar,

Kai LiAuthors Info & Claims

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

Pages 208 - 217

https://doi.org/10.1145/1031171.1031213

Published: 13 November 2004 Publication History

Abstract

The recent theoretical advances on compact data structures (also called "sketches") have raised the question of whether they can effectively be applied to content-based image retrieval systems. The main challenge is to derive an algorithm that achieves high-quality similarity searches while using compact metadata. This paper proposes a new similarity search method consisting of three parts. The first is a new region feature representation with weighted $=<i></i><inf>1</inf> distance function, and EMD* match, an improved EMD match, to compute image similarity. The second is a thresholding and transformation algorithm to convert feature vectors into very compact data structures. The third is an EMD embedding based filtering method to speed up the query process. We have implemented a prototype system with the proposed method and performed experiments with a 10,000 image database. Our results show that the proposed method can achieve more effective similarity searches than previous approaches with metadata 3 to 72 times more compact than previous systems. The experiments also show that our EMD embedding based filtering technique can speed up the query process by a factor of 5 or more with little loss in query effectiveness.

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Cited By

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Index Terms

Image similarity search with compact data structures
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge management

November 2004

678 pages

ISBN:1581138741

DOI:10.1145/1031171

General Chair:
David Grossman
Illinois Institute of Technology
,
Program Chairs:
Luis Gravano
Columbia University
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign
,
Otthein Herzog
University of Bremen, Germany
,
David A. Evans
Clairvoyance Corporation

Copyright © 2004 ACM.

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Published: 13 November 2004

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https://doi.org/10.1007/s11042-023-14825-z
Luo CHuang GChen XChen Y(2022)Registration-Free Multicomponent Joint AVA Inversion Using Optimal TransportIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.306327160(1-13)Online publication date: 2022
https://doi.org/10.1109/TGRS.2021.3063271
Mic VZezula P(2021)On the Similarity Search With Hamming Space SketchesIntelligent Analytics With Advanced Multi-Industry Applications10.4018/978-1-7998-4963-6.ch005(97-127)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-4963-6.ch005
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