peking-university-landmarks-a-context-aware-visual-search-benchmark-database

1. INTERNATIONAL ORGANISATION FOR STANDARDISATION
ORGANISATION INTERNATIONALE DE NORMALISATION
ISO/IEC JTC1/SC29/WG11
CODING OF MOVING PICTURES AND AUDIO
ISO/IEC JTC1/SC29/WG11
MPEG2011/m19188
January 2011, Daegu, Korea
Source Peking University, Harbin Institute of Technology, China
Status Input Contribution
Title Peking University Landmarks: A Context Aware Visual Search Benchmark Database
Author lingyu@pku.edu.cn, Lingyu Duan
jirongrong@gmail.com, Rongrong Ji
cjie@pku.edu.cn, Jie Chen
syang@pku.edu.cn, Shuang Yang
tjhuang@pku.edu.cn, Tiejun Huang
hongxun.yao@gmail.com, Hongxun Yao
wgao@pku.edu.cn, Wen Gao
1 Introduction
The 93rd
MPEG meeting output draft requirements documents (w11529, w11530 and w11531) of
Compact Descriptors for Visual Search. To advance this work, this contribution presents our
work on establishing context aware visual search benchmark database for mobile Landmark
search. In the input contribution m18542 at MPEG 94th
Meeting [9], Peking University has
proposed a compact descriptor for visual search, which combines location cues to learn a
discriminative and compact visual descriptor that is very suitable for mobile landmark search.
We believe our practice as well as the benchmark dataset would enhance the use cases and be
helpful to identify requirements for Compact Descriptors for Visual Search.
While there are ever growing focuses on mobile visual search in recent years, a
comprehensive benchmark database for fair evaluation among different strategies is still missing.
In particular, the rich contextual cues in mobile devices, such as GPS information and camera
parameters, are left unexploited in the current visual search benchmarks. This contribution
introduces a Peking University Landmarks benchmark for the quantitative evaluations of mobile
visual search performance with the support of GPS information. It contains over 13179 images
organized into 198 distinct landmark locations within the Peking University campus, which is
built by 20 volunteers during November and December, 2010. Each location is captured with
multiple shot sizes and viewing angles, using both digital cameras and phone cameras, each
photo being tagged with rich contextual information in the mobile scenarios. Moreover, this
benchmark studies typical quality degeneration scenarios in mobile photographing, including
variable resolutions, blurring, lighting changes, occlusions, as well as various viewing angles.
Together with this benchmark, we provide the bag-of-visual-words search baselines in the cases
of using either spatial or contextual information in returning image ranking. Finally, distractor
images are further introduced to evaluate the robustness of visual search methods in the database.

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4. without visual ranking. Over PKUBench, we pay more focus to the use of such contextual cues
in facilitating visual search, including: (1) GPS tag (both latitude and longitude); (2) landmark
name label; (3) shot size (long, medium, and close-up) and viewpoints (frontal, side, and others)
of those photos; (4) camera type (digital camera or mobile phone camera); (5) capture time
stamp. We also provide EXIF information: camera setting (focal, resolution).
In addition, we will show the performance improvement of using contextual information by
providing baselines that leverage GPS to refine visual ranking. Furthermore, the effects of less
precise contextual information are also investigated by adding distractor images by imposing
random GPS distortion to the original GPS location of an image.
Scene Diversity: We provide as diverse landmark appearances as possible to simulate the
real-world difficulty in visual search. Hence, the volunteers are encouraged to capture both
queries and the ground truth photos (for both digital and phone cameras) without any particular
intent to avoid the intruding foreground, e.g. cars, human faces, and tree occlusions.
4 Comparing with Related Benchmarks
Zubud Database [2] is widely adopted to evaluate vision-based geographical location
recognition, which contains 1,005 color images of 201 buildings or scenes (5 images per
building or per scene) in Zurich city, Switzerland.
Oxford Buildings Database [3] contains 5,062 images collected from Flickr by
searching for particular Oxford landmarks, with manual annotated ground truth for 11 different
landmarks, each represented by 5 possible queries.
SCity Database [4] contains 20, 000 street-side photos for mobile visual search
validation in Microsoft Photo2Search system [4]. It is captured along the Seattle urban streets by
a car automatically, taken from the main streets of Seattle by a car with six surrounding cameras
and a GPS device. The location of each captured photo is obtained by aligning the time stamps
of photos and GPS record.
UKBench Database [5] contains 10,000 images with 2,500 objects, containing indoor
objects like CD Covers, book set, etc. There are four images per object to offer sufficient
variances in viewpoints, rotations, lighting conditions, scales, occlusions, and affine transforms.
Stanford Mobile Visual Search Data Set [6] contains camera-phone images of
products, CDs, books, outdoor landmarks, business cards, text documents, museum paintings and
video clips. It provides several unique characteristics, e.g. varying lighting conditions,
perspective distortion, and mobile phone queries.
Table 1. Brief comparison of related benchmarking databases.
Database PKUBench Zubud Oxfold SCity UKBench Stanford
Data Scale 13,179 1,005 5,062 20,000 10,000
Images
Per Landmark
/Object Category
66 5 92 6 4
Mobile Capture √ × × × × √
Categorized
shot size,
view Angle,
landmark/Object
Scale
√ × × × Indoor ×
Blurring Query √ × × × × ×
Context √ × × × × ×

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13. 7 Application Scenarios
We brief possible application scenarios of our Peking University Landmarks database as follows:
A benchmark dataset for mobile visual search: We hope the Peking University Landmarks
could become useful resource to validate mobile visual search systems. It emphasizes two
important factors in mobile visual search: query quality and contextual cues. To the best of our
knowledge, both are beyond the state-of-the-art benchmark databases. In addition, it offers a
dataset to evaluate the effectiveness and robustness of contextual information.
A benchmark dataset for location recognition: This dataset can be used to evaluate
traditional location recognition systems since GPS location are bound with each image instance
A training resource for scene modeling: This dataset may facilitate scene analysis and
modeling since our photograph is well designed to cover multi-shot, multi-view appearances of
the landmarks of multi-scale. To this end, we will provide the camera calibration information in
our future work.
A training resource to learn better photograph manners: Our landmark photo collection
can be further exploited to learn the (recommended) mobile visual photograph manners (proper
angle and shot size for different types of landmarks) towards better visual search results.
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