Blog
The latest from Google Research
Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval
Friday, May 3, 2019
Posted by Bingyi Cao and Tobias Weyand, Software Engineers, Google AI
Last year we released
Google-Landmarks
, the largest world-wide landmark recognition dataset available at that time. In order to foster advancements in research on
instance-level recognition
(recognizing specific instances of objects, e.g. distinguishing Niagara Falls from just any waterfall) and
image retrieval
(matching a specific object in an input image to all other instances of that object in a catalog of reference images), we also hosted two
Kaggle challenges
,
Landmark Recognition 2018
and
Landmark Retrieval 2018
, in which more than 500 teams of researchers and
machine learning
(ML) enthusiasts participated. However, both instance recognition and image retrieval methods require ever larger datasets in both the number of images and the variety of landmarks in order to train better and more robust systems.
In support of this goal, this year we are releasing
Google-Landmarks-v2
, a completely new, even larger landmark recognition dataset that includes over 5 million images (2x that of the first release) of more than 200 thousand different landmarks (an increase of 7x). Due to the difference in scale, this dataset is much more diverse and creates even greater challenges for state-of-the-art instance recognition approaches. Based on this new dataset, we are also announcing two new Kaggle challenges—
Landmark Recognition 2019
and
Landmark Retrieval 2019
—and releasing the
source code and model
for
Detect-to-Retrieve
, a novel image representation suitable for retrieval of specific object instances.
Heatmap of the landmark locations in Google-Landmarks-v2, which demonstrates the increase in the scale of the dataset and the improved geographic coverage compared to
last year’s dataset
.
Creating the Dataset
A particular problem in preparing Google-Landmarks-v2 was the generation of instance labels for the landmarks represented, since it is virtually impossible for annotators to recognize all of the hundreds of thousands of landmarks that could potentially be present in a given photo. Our solution to this problem was to crowdsource the landmark labeling through the efforts of a world-spanning community of hobby photographers, each familiar with the landmarks in their region.
Selection of images from Google-Landmarks-v2. Landmarks include (left to right, top to bottom)
Neuschwanstein Castle,
Golden Gate Bridge
,
Kiyomizu-dera
,
Burj khalifa
,
Great Sphinx of Giza
, and
Machu Picchu
.
Another issue for research datasets is the requirement that images be shared freely and stored indefinitely, so that the dataset can be used to track the progress of research over a long period of time. As such, we sourced the Google-Landmarks-v2 images through
Wikimedia Commons
, capturing both world-famous and lesser-known, local landmarks while ensuring broad geographic coverage (thanks in part to
Wiki Loves Monuments
) and photos sourced from public institutions, including historical photographs that are valuable to test
instance recognition
over time.
The Kaggle Challenges
The goal of the
Landmark Recognition 2019
challenge is to recognize a landmark presented in a query image, while the goal of
Landmark Retrieval 2019
is to find all images showing that landmark. The challenges include cash prizes totaling $50,000 and the winning teams will be invited to present their methods at the
Second Landmark Recognition Workshop
at
CVPR 2019
.
Open Sourcing our Model
To foster research reproducibility and help push the field of instance recognition forward, we are also releasing
open-source code
for our new technique, called
Detect-to-Retrieve
(which will be presented as a paper in
CVPR 2019
). This new method leverages
bounding boxes
from an object detection model to give extra weight to image regions containing the class of interest, which significantly improves accuracy. The model we are releasing is trained on a subset of 86k images from the original Google-Landmarks dataset that were annotated with landmark bounding boxes. We are making these annotations available along with the original dataset
here
.
We invite researchers and ML enthusiasts to participate in the
Landmark Recognition 2019
and
Landmark Retrieval 2019
Kaggle challenges and to join the
Second Landmark Recognition Workshop
at
CVPR 2019
. We hope that this dataset will help advance the state-of-the-art in
instance recognition
and
image retrieval
. The data is being made available via the
Common Visual Data Foundation
.
Acknowledgments
The core contributors to this project are Andre Araujo, Bingyi Cao, Jack Sim and Tobias Weyand. We would like to thank our team members Daniel Kim, Emily Manoogian, Nicole Maffeo, and Hartwig Adam for their kind help. Thanks also to Marvin Teichmann and Menglong Zhu for their contribution to collecting the landmark bounding boxes and developing the Detect-to-Retrieve technique. We would like to thank Will Cukierski and Maggie Demkin for their help organizing the Kaggle challenge, Elan Hourticolon-Retzler, Yuan Gao, Qin Guo, Gang Huang, Yan Wang, Zhicheng Zheng for their help with data collection, Tsung-Yi Lin for his support with CVDF hosting, as well as our CVPR workshop co-organizers Bohyung Han, Shih-Fu Chang, Ondrej Chum, Torsten Sattler, Giorgos Tolias, and Xu Zhang. We have great appreciation for the Wikimedia Commons Community and their volunteer contributions to an invaluable photographic archive of the world’s cultural heritage. And finally, we’d like to thank the Common Visual Data Foundation for hosting the dataset.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2020
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
