Forward Index Compression for Instance Retrieval in an Augmented Reality Application

Qi Wang; Michal Siedlaczek; Yen Yu Chen; Michael Gormish; Torsten Suel

doi:10.1109/BigData47090.2019.9006023

Forward Index Compression for Instance Retrieval in an Augmented Reality Application

Qi Wang, Michal Siedlaczek, Yen Yu Chen, Michael Gormish, Torsten Suel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Instance retrieval systems are widely used in applications such as robot navigation, medical diagnosis, and augmented reality. Blippar is a company that creates compelling augmented reality experiences or provides you with the tools to build your own. In this paper we focus on one of the company's augmented-reality applications, with which users are able to point their phone cameras at different objects in order to receive information about the objects in real time. In this paper, we provide what we believe to be the first study of forward index compression techniques for such instance retrieval systems. First, we perform an analysis of real-world data from a large-scale commercial instance retrieval system, run by Blippar focusing on augmented reality. Then we propose an entropy-based lossless compression strategy. Experiments show that our proposed Huffman-based approach outperforms a variety of other compression techniques, while also increasing overall system efficiency slightly.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1946-1952
Number of pages	7
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006023
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Keywords

Augmented Reality
Index Compression
Instance Retrieval
Retrieval Efficiency

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems
Information Systems and Management

Access to Document

10.1109/BigData47090.2019.9006023

Cite this

Wang, Q., Siedlaczek, M., Chen, Y. Y., Gormish, M., & Suel, T. (2019). Forward Index Compression for Instance Retrieval in an Augmented Reality Application. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 1946-1952). Article 9006023 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006023

Forward Index Compression for Instance Retrieval in an Augmented Reality Application. / Wang, Qi; Siedlaczek, Michal; Chen, Yen Yu et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1946-1952 9006023 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, Q, Siedlaczek, M, Chen, YY, Gormish, M & Suel, T 2019, Forward Index Compression for Instance Retrieval in an Augmented Reality Application. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006023, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1946-1952, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006023

Wang Q, Siedlaczek M, Chen YY, Gormish M, Suel T. Forward Index Compression for Instance Retrieval in an Augmented Reality Application. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1946-1952. 9006023. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006023

Wang, Qi ; Siedlaczek, Michal ; Chen, Yen Yu et al. / Forward Index Compression for Instance Retrieval in an Augmented Reality Application. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1946-1952 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

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title = "Forward Index Compression for Instance Retrieval in an Augmented Reality Application",

abstract = "Instance retrieval systems are widely used in applications such as robot navigation, medical diagnosis, and augmented reality. Blippar is a company that creates compelling augmented reality experiences or provides you with the tools to build your own. In this paper we focus on one of the company's augmented-reality applications, with which users are able to point their phone cameras at different objects in order to receive information about the objects in real time. In this paper, we provide what we believe to be the first study of forward index compression techniques for such instance retrieval systems. First, we perform an analysis of real-world data from a large-scale commercial instance retrieval system, run by Blippar focusing on augmented reality. Then we propose an entropy-based lossless compression strategy. Experiments show that our proposed Huffman-based approach outperforms a variety of other compression techniques, while also increasing overall system efficiency slightly.",

keywords = "Augmented Reality, Index Compression, Instance Retrieval, Retrieval Efficiency",

author = "Qi Wang and Michal Siedlaczek and Chen, {Yen Yu} and Michael Gormish and Torsten Suel",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006023",

language = "English (US)",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1946--1952",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

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AU - Wang, Qi

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AU - Chen, Yen Yu

AU - Gormish, Michael

AU - Suel, Torsten

PY - 2019/12

Y1 - 2019/12

N2 - Instance retrieval systems are widely used in applications such as robot navigation, medical diagnosis, and augmented reality. Blippar is a company that creates compelling augmented reality experiences or provides you with the tools to build your own. In this paper we focus on one of the company's augmented-reality applications, with which users are able to point their phone cameras at different objects in order to receive information about the objects in real time. In this paper, we provide what we believe to be the first study of forward index compression techniques for such instance retrieval systems. First, we perform an analysis of real-world data from a large-scale commercial instance retrieval system, run by Blippar focusing on augmented reality. Then we propose an entropy-based lossless compression strategy. Experiments show that our proposed Huffman-based approach outperforms a variety of other compression techniques, while also increasing overall system efficiency slightly.

AB - Instance retrieval systems are widely used in applications such as robot navigation, medical diagnosis, and augmented reality. Blippar is a company that creates compelling augmented reality experiences or provides you with the tools to build your own. In this paper we focus on one of the company's augmented-reality applications, with which users are able to point their phone cameras at different objects in order to receive information about the objects in real time. In this paper, we provide what we believe to be the first study of forward index compression techniques for such instance retrieval systems. First, we perform an analysis of real-world data from a large-scale commercial instance retrieval system, run by Blippar focusing on augmented reality. Then we propose an entropy-based lossless compression strategy. Experiments show that our proposed Huffman-based approach outperforms a variety of other compression techniques, while also increasing overall system efficiency slightly.

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KW - Index Compression

KW - Instance Retrieval

KW - Retrieval Efficiency

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M3 - Conference contribution

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 1946

EP - 1952

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

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A2 - Barga, Roger

A2 - Zaniolo, Carlo

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Forward Index Compression for Instance Retrieval in an Augmented Reality Application

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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