Blog
The latest news from Google AI
Google-Landmarks: A New Dataset and Challenge for Landmark Recognition
Thursday, March 1, 2018
Posted by André Araujo and Tobias Weyand, Software Engineers, Google Research
Image classification technology has shown remarkable improvement over the past few years, exemplified in part by the
Imagenet
classification challenge, where error rates continue to
drop substantially every year
. In order to continue advancing the state of the art in computer vision, many researchers are now putting more focus on fine-grained and instance-level recognition problems – instead of recognizing general entities such as buildings, mountains and (of course) cats, many are designing machine learning algorithms capable of identifying the Eiffel Tower, Mount Fuji or Persian cats. However, a significant obstacle for research in this area has been the lack of large annotated datasets.
Today, we are excited to advance instance-level recognition by releasing Google-Landmarks, the largest worldwide dataset for recognition of human-made and natural landmarks. Google-Landmarks is being released as part of the
Landmark Recognition
and
Landmark Retrieval
Kaggle challenges, which will be the focus of the
CVPR’18 Landmarks workshop
. The dataset contains more than 2 million images depicting 30 thousand unique landmarks from across the world (their geographic distribution is presented below), a number of classes that is ~30x larger than what is available in commonly used datasets. Additionally, to spur research in this field, we are
open-sourcing
Deep Local Features (
DELF
), an attentive local feature descriptor that we believe is especially suited for this kind of task.
Geographic distribution of landmarks in our dataset.
Landmark recognition presents some noteworthy differences from other problems. For example, even within a large annotated dataset, there might not be much training data available for some of the less popular landmarks. Additionally, since landmarks are generally rigid objects which do not move, the intra-class variation is very small (in other words, a landmark’s appearance does not change that much across different images of it). As a result, variations only arise due to image capture conditions, such as occlusions, different viewpoints, weather and illumination, making this distinct from other image recognition datasets where images of a particular class (such as a dog) can vary much more. These characteristics are also shared with other instance-level recognition problems, such as
artwork recognition
— so we hope the new dataset can benefit research for other image recognition problems as well.
The two Kaggle challenges provide access to annotated data to help researchers address these problems. The
recognition
track challenge is to build models that recognize the correct landmark in a dataset of challenging test images, while the
retrieval
track challenges participants to retrieve images containing the same landmark.
A few examples of images from the Google-Landmarks dataset, including landmarks such as
Big Ben
,
Sacre Coeur Basilica
, the
rock sculpture of Decebalus
and the
Megyeri Bridge
, among others.
If you plan to be at
CVPR
this year, we hope you’ll attend the
CVPR’18 Landmarks workshop
. However, everyone is able to participate in the challenge, and access to the new dataset is available via the Kaggle website. We hope this resource is valuable to your research and we can’t wait to see the ideas you will come up with for recognizing landmarks!
Acknowledgments
Jack Sim, Will Cukierski, Maggie Demkin, Hartwig Adam, Bohyung Han, Shih-Fu Chang, Ondrej Chum, Torsten Sattler, Giorgos Tolias, Xu Zhang, Fernando Brucher, Marco Andreetto, Gursheesh Kour.
Labels
2018
2019
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
2020
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
.
