Blog
The latest from Google Research
Improving Photo Search: A Step Across the Semantic Gap
Wednesday, June 12, 2013
Posted by Chuck Rosenberg, Image Search Team
Last month at
Google I/O
, we showed a
major upgrade to the photos experience
: you can now easily
search your own photos
without having to manually label each and every one of them. This is powered by computer vision and machine learning technology, which uses the visual content of an image to generate searchable tags for photos combined with other sources like text tags and EXIF metadata to enable search across thousands of concepts like a flower, food, car, jet ski, or turtle.
For
many years
Google has offered
Image Search
over web images; however, searching across photos represents a difficult new challenge. In Image Search there are many pieces of information which can be used for ranking images, for example text from the web or the image filename. However, in the case of photos, there is typically little or no information beyond the pixels in the images themselves. This makes it harder for a computer to identify and categorize what is in a photo. There are some things a computer can do well, like recognize rigid objects and handwritten digits. For other classes of objects, this is a daunting task, because the average toddler is better at understanding what is in a photo than the world’s most powerful computers running state of the art algorithms.
This past October the state of the art seemed to move things a bit closer to toddler performance. A system which used
deep learning
and
convolutional neural networks
easily beat out more traditional approaches in the
ImageNet computer vision competition
designed to test image understanding. The
winning team
was from
Professor Geoffrey Hinton
’s group at the University of Toronto.
We built and trained models similar to those from the winning team using
software infrastructure
for training large-scale neural networks developed at Google in a group started by
Jeff Dean
and
Andrew Ng
. When we evaluated these models, we were impressed; on our test set we saw double the average precision when compared to other approaches we had tried. We knew we had found what we needed to make photo searching easier for people using Google. We
acquired the rights to the technology
and went full speed ahead adapting it to run at large scale on Google’s computers. We took cutting edge research straight out of an academic research lab and launched it, in just a little over six months. You can try it out at
photos.google.com
.
Why the success now? What is new? Some things are unchanged: we still use convolutional neural networks -- originally developed in the late 1990s by
Professor Yann LeCun
in the context of software for
reading handwritten letters and digits
. What is different is that both computers and algorithms have improved significantly. First, bigger and faster computers have made it feasible to train larger neural networks with much larger data. Ten years ago, running neural networks of this complexity would have been a momentous task even on a single image -- now we are able to run them on billions of images. Second, new training techniques have made it possible to train the large deep neural networks necessary for successful image recognition.
We feel it would be interesting to the research community to discuss some of the unique aspects of the system we built and some qualitative observations we had while testing the system.
The first is our label and training set and how it compares to that used in the
ImageNet Large Scale Visual Recognition
competition. Since we were working on search across photos, we needed an appropriate label set. We came up with a set of about 2000 visual classes based on the most popular labels on Google+ Photos and which also seemed to have a visual component, that a human could recognize visually. In contrast, the ImageNet competition has 1000 classes. As in ImageNet, the classes were not text strings, but are
entities
, in our case we use
Freebase entities
which form the basis of the
Knowledge Graph
used in Google search. An entity is a way to uniquely identify something in a language-independent way. In English when we encounter the word “jaguar”, it is hard to determine if it represents the animal or the car manufacturer. Entities assign a unique ID to each, removing that ambiguity, in this case “
/m/0449p
” for the former and “
/m/012x34
” for the latter. In order to train better classifiers we used more training images per class than ImageNet, 5000 versus 1000. Since we wanted to provide only high precision labels, we also refined the classes from our initial set of 2000 to the most precise 1100 classes for our launch.
During our development process we had many more qualitative observations we felt are worth mentioning:
1) Generalization performance. Even though there was a significant difference in visual appearance between the training and test sets, the network appeared to generalize quite well. To train the system, we used images mined from the web which did not match the typical appearance of personal photos. Images on the web are often used to illustrate a single concept and are carefully composed, so an image of a flower might only be a close up of a single flower. But personal photos are unstaged and impromptu, a photo of a flower might contain many other things in it and may not be very carefully composed. So our training set image distribution was not necessarily a good match for the distribution of images we wanted to run the system on, as the examples below illustrate. However, we found that our system trained on web images was able to generalize and perform well on photos.
A typical photo of a flower found on the web.
A typical photo of a flower found in an impromptu photo.
2) Handling of classes with multi-modal appearance. The network seemed to be able to handle classes with multimodal appearance quite well, for example the “car” class contains both exterior and interior views of the car. This was surprising because the final layer is effectively a
linear classifier
which creates a single dividing plane in a high dimensional space. Since it is a single plane, this type of classifier is often not very good at representing multiple very different concepts.
3) Handling abstract and generic visual concepts. The system was able to do reasonably well on classes that one would think are somewhat abstract and generic. These include "dance", "kiss", and "meal", to name a few. This was interesting because for each of these classes it did not seem that there would be any simple visual clues in the image that would make it easy to recognize this class. It would be difficult to describe them in terms of simple basic visual features like color, texture, and shape.
Photos recognized as containing a meal.
4) Reasonable errors. Unlike other systems we experimented with, the errors which we observed often seemed quite reasonable to people. The mistakes were the type that a person might make - confusing things that look similar. Some people have already noticed this, for example,
mistaking a goat for a dog or a millipede for a snake
. This is in contrast to other systems which often make errors which seem nonsensical to people, like mistaking a tree for a dog.
Photo of a banana slug mistaken for a snake.
Photo of a donkey mistaken for a dog.
5) Handling very specific visual classes. Some of the classes we have are very specific, like specific types of flowers, for example “hibiscus” or “dhalia”. We were surprised that the system could do well on those. To recognize specific subclasses very fine detail is often needed to differentiate between the classes. So it was surprising that a system that could do well on a full image concept like “sunsets” could also do well on very specific classes.
Photo recognized as containing a hibiscus flower.
Photo recognized as containing a dahlia flower.
Photo recognized as containing a polar bear.
Photo recognized as containing a grizzly bear.
The resulting computer vision system worked well enough to launch to people as a useful tool to help improve personal photo search, which was a big step forward. So, is computer vision solved? Not by a long shot. Have we gotten computers to see the world as well as people do? The answer is not yet, there’s still a lot of work to do, but we’re closer.
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
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
.
