Abstract
High quality test collections have been becoming more and more important for the technological advancement in geo-referenced image retrieval and analytics. In this paper, we present a large scale test collection to support robust performance evaluation of landmark image search and corresponding construction methodology. Using the approach, we develop a very large scale test collection consisting of three key components: (1) 355,141 images of 128 landmarks in five cities across three continents crawled 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. In order to support robust and effective performance assessment, a series of baseline experimental studies have been conducted on the search performance over both textual and visual queries. The results demonstrate importance and effectiveness of the test collection.
| Original language | English |
|---|---|
| Pages (from-to) | 13-26 |
| Number of pages | 14 |
| Journal | Signal Processing |
| Volume | 124 |
| Early online date | 20 Nov 2015 |
| DOIs | |
| Publication status | Published - Jul 2016 |
| Externally published | Yes |
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On very large scale test collection for landmark image search benchmarking. / Cheng, Zhiyong; Shen, Jialie.
In: Signal Processing, Vol. 124, 07.2016, p. 13-26.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - On very large scale test collection for landmark image search benchmarking
AU - Cheng, Zhiyong
AU - Shen, Jialie
PY - 2016/7
Y1 - 2016/7
N2 - High quality test collections have been becoming more and more important for the technological advancement in geo-referenced image retrieval and analytics. In this paper, we present a large scale test collection to support robust performance evaluation of landmark image search and corresponding construction methodology. Using the approach, we develop a very large scale test collection consisting of three key components: (1) 355,141 images of 128 landmarks in five cities across three continents crawled 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. In order to support robust and effective performance assessment, a series of baseline experimental studies have been conducted on the search performance over both textual and visual queries. The results demonstrate importance and effectiveness of the test collection.
AB - High quality test collections have been becoming more and more important for the technological advancement in geo-referenced image retrieval and analytics. In this paper, we present a large scale test collection to support robust performance evaluation of landmark image search and corresponding construction methodology. Using the approach, we develop a very large scale test collection consisting of three key components: (1) 355,141 images of 128 landmarks in five cities across three continents crawled 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. In order to support robust and effective performance assessment, a series of baseline experimental studies have been conducted on the search performance over both textual and visual queries. The results demonstrate importance and effectiveness of the test collection.
KW - Large scale landmark image search
KW - Performance evaluation
UR - http://www.scopus.com/inward/record.url?scp=84960229929&partnerID=8YFLogxK
U2 - 10.1016/j.sigpro.2015.10.037
DO - 10.1016/j.sigpro.2015.10.037
M3 - Article
VL - 124
SP - 13
EP - 26
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
ER -