Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search

Zhiyong Cheng; Jing Ren; Jialie Shen; Haiyan Miao

doi:10.1007/978-3-642-35728-2_4

Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search

Zhiyong Cheng, Jing Ren, Jialie Shen, Haiyan Miao

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

19 Citations (Scopus)

Abstract

Studying and analyzing system performance is one of the fundamental factors for the related technological advancement in image retrieval. In this paper, we report the construction of a large scale test collection for facilitating robust performance evaluation of mobile landmark image search. Totally, the test collection consists of (1) 355,141 images about 128 landmarks in five cities over 3 continents from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader and EXIF); and (3) six types of low-level visual features. For the task of landmark image retrieval evaluation, we also conduct a series of baseline experimental studies on the search performance over different visual queries, which represent different views of a landmark.

Original language	English
Title of host publication	Advances in Multimedia Modeling
Subtitle of host publication	19th International Conference, MMM 2013, Proceedings
Editors	Shipeng Li, Abdulmotaleb El Saddik, Meng Wang, Tao Mei, Nicu Sebe, Shuicheng Yan, Richang Hong, Cathal Gurrin
Publisher	Springer
Pages	36-46
Number of pages	11
Edition	1
ISBN (Electronic)	9783642357282
ISBN (Print)	9783642357275
DOIs	https://doi.org/10.1007/978-3-642-35728-2_4
Publication status	Published - 7 Jan 2013
Externally published	Yes
Event	19th International Conference on Advances in Multimedia Modeling - Huangshan, China Duration: 7 Jan 2013 → 9 Jan 2013 Conference number: 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7733 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Advances in Multimedia Modeling
Abbreviated title	MMM 2013
Country/Territory	China
City	Huangshan
Period	7/01/13 → 9/01/13

Access to Document

10.1007/978-3-642-35728-2_4Licence: Unspecified

Cite this

Cheng, Z., Ren, J., Shen, J., & Miao, H. (2013). Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search. In S. Li, A. El Saddik, M. Wang, T. Mei, N. Sebe, S. Yan, R. Hong, & C. Gurrin (Eds.), Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Proceedings (1 ed., pp. 36-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7733 LNCS, No. PART 2). Springer. https://doi.org/10.1007/978-3-642-35728-2_4

Cheng, Zhiyong ; Ren, Jing ; Shen, Jialie et al. / Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search. Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Proceedings. editor / Shipeng Li ; Abdulmotaleb El Saddik ; Meng Wang ; Tao Mei ; Nicu Sebe ; Shuicheng Yan ; Richang Hong ; Cathal Gurrin. 1. ed. Springer, 2013. pp. 36-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search",

abstract = "Studying and analyzing system performance is one of the fundamental factors for the related technological advancement in image retrieval. In this paper, we report the construction of a large scale test collection for facilitating robust performance evaluation of mobile landmark image search. Totally, the test collection consists of (1) 355,141 images about 128 landmarks in five cities over 3 continents from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader and EXIF); and (3) six types of low-level visual features. For the task of landmark image retrieval evaluation, we also conduct a series of baseline experimental studies on the search performance over different visual queries, which represent different views of a landmark.",

keywords = "Mobile landmark search, Performance evaluation",

author = "Zhiyong Cheng and Jing Ren and Jialie Shen and Haiyan Miao",

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Cheng, Z, Ren, J, Shen, J & Miao, H 2013, Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search. in S Li, A El Saddik, M Wang, T Mei, N Sebe, S Yan, R Hong & C Gurrin (eds), Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Proceedings. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7733 LNCS, Springer, pp. 36-46, 19th International Conference on Advances in Multimedia Modeling, Huangshan, China, 7/01/13. https://doi.org/10.1007/978-3-642-35728-2_4

Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search. / Cheng, Zhiyong; Ren, Jing; Shen, Jialie et al.

Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Proceedings. ed. / Shipeng Li; Abdulmotaleb El Saddik; Meng Wang; Tao Mei; Nicu Sebe; Shuicheng Yan; Richang Hong; Cathal Gurrin. 1. ed. Springer, 2013. p. 36-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7733 LNCS, No. PART 2).

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

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N2 - Studying and analyzing system performance is one of the fundamental factors for the related technological advancement in image retrieval. In this paper, we report the construction of a large scale test collection for facilitating robust performance evaluation of mobile landmark image search. Totally, the test collection consists of (1) 355,141 images about 128 landmarks in five cities over 3 continents from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader and EXIF); and (3) six types of low-level visual features. For the task of landmark image retrieval evaluation, we also conduct a series of baseline experimental studies on the search performance over different visual queries, which represent different views of a landmark.

AB - Studying and analyzing system performance is one of the fundamental factors for the related technological advancement in image retrieval. In this paper, we report the construction of a large scale test collection for facilitating robust performance evaluation of mobile landmark image search. Totally, the test collection consists of (1) 355,141 images about 128 landmarks in five cities over 3 continents from Flickr; (2) different kinds of textual features for each image, including surrounding text (e.g. tags), contextual data (e.g. geo-location and upload time), and metadata (e.g. uploader and EXIF); and (3) six types of low-level visual features. For the task of landmark image retrieval evaluation, we also conduct a series of baseline experimental studies on the search performance over different visual queries, which represent different views of a landmark.

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A2 - Hong, Richang

A2 - Gurrin, Cathal

PB - Springer

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ER -

Cheng Z, Ren J, Shen J, Miao H. Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search. In Li S, El Saddik A, Wang M, Mei T, Sebe N, Yan S, Hong R, Gurrin C, editors, Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Proceedings. 1 ed. Springer. 2013. p. 36-46. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-35728-2_4

Building a Large Scale Test Collection for Effective Benchmarking of Mobile Landmark Search

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