Large dataset for object and scene recognition A. Torralba, R. Fergus, W. T. Freeman 80 million tiny...
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Transcript of Large dataset for object and scene recognition A. Torralba, R. Fergus, W. T. Freeman 80 million tiny...
Large dataset for object and scene recognition
A. Torralba, R. Fergus, W. T. Freeman
80 million tiny images
Ron Yanovich Guy Peled
http://royal.pingdom.com/
Internet 2012 in numbers• 7 petabytes
– How much photo content Facebook added every month.
• 300 million– Number of new photos added every day to Facebook.
• 5 billion– The total number of photos uploaded to Instagram since its start,
reached in September 2012.
• 58 – Number of photos uploaded every second to Instagram.
• 1 – Apple iPhone 4S was the most popular camera on Flickr.
• Image search is a specialized data search used to find images
• Search methods– Image meta search– Content-base image retrieval
Image Retrieval
• Search of images based on associated metadata such as keywords, text, etc.
• Google Images– The keywords for the image search are based on
the filename of the image, the link text pointing to the image, and text adjacent to the image
Image meta search
http://en.wikipedia.org/wiki/Google_Images
• The search will analyze the actual contents of the image by colors, shapes, textures etc.
• The most common method for comparing two images in content based image retrieval is using an image distance measure.
• Many CBIR systems have been developed, but the problem of retrieving images on the basis of their pixel content remains largely unsolved.
Content-based image retrieval (CBIR)
http://en.wikipedia.org/wiki/Content-based_image_retrieval
prag.diee.unica.itwww.dailydawdle.com
Why not combine both methods?
Primary goals
• 79,000,000 images collected from WWW
• Image matching similar to Google search prediction– “Did you mean?” tool
The problem• 79,000,000 images
– Large storage
– Long process time
Collecting ~80,000,000 images
• Using image search engines:– Altavista, Ask, Flickr, Cydral, Google, Picsearch and
Webshots
• 760GB on one hard disk?
www.apartmenttherapy.com
Creating image dataset
• Each image is labeled with one of the 75,062 non-abstract nouns in English,
as listed in the Wordnetlexical database.
• The result is a large semantic tree
What is WordNet
• WordNet® is a large lexical database of English Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept.
http://wordnet.princeton.edu
carrot
Plant root
Plant organ
Plant part
Natural object
Object, physical object
entity, physical thing
entity
mechanism
Mechanical device
sprinkler
http://www.cs.princeton.edu/courses/archive/spr07/cos226/assignments/wordnet.html
Reduce space and process time
With The size of 32X32 we can get more than 80% correct recognition rate
Reduce space and process time
• Moving from 256X256 to 32X32
Reduce space and process time
Studies on the face perception have shown that only 16X16 pixels needed for robust face recognition
This remarkable performance is also found in a scene recognition task
Reduce space and process time
• Speech recognition uses 10^6 data points.
• Current experiments in object recognition typically use 10^2 - 10^4
Reduce space and process timeHuman visual space
• ( 100 years ) * ( 30 frames per sec ) = 10^11
• All 32X32 images = 10^7400 images– Most of the images are just noise
Reduce space and process time
• We understand that 32^2 contain enough data for our purpose.
• The advantage is the ability to work with million of images (~10^8).
Statistics of low-res images
• Image matching methods:
– SSD (sum of squared differences)
– Warp
– Shift (per pixel)
Statistics of low-res images
Statistics of low-res images
Recognition
• The goal is to recognize objects and scenery by using SSD, WARP, SHIFT methods
instead of complex matching algorithms
• Given an image, the neighbors are found using some similarity measure (D-Shift)
Recognition
• Each neighbor in turn votes for its branch within the WordNet tree.
• Classification
• Image Search returns an object
Person detection
• Is it a person?
Person detection
• Standard approach :
Face detection algorithm
Not good enough
Person detection
• Better approach: Using the image DB
• More then 23% images contain pictures of people
Person detection
• Evaluating performance by two different sets of test images:
- Evaluation using randomly drawn images
- Evaluation using Altavista images
Evaluation using randomly drawn images
• Randomly drawn 1,125 images from DB• People were manually segmented on each
image
• Findings:– Large Appearance Better performance– Weaker labels Largest object
Large Appearance Better Performance
A better performance is achieved when a person’s appearance is greater than 20% of the image.
Evaluation using Altavista images
• 1,018 images drawn by searching ‘person’ label• Images classified using WordNetReordered labels
Scene recognition
• A search for images that match an entire scene rather than a specific object
• Randomly tagging 1,125 pictures to:“City” , “River” , “Field” , “Mountain”
DB Size:80,000,000800,0008,000
The larger the database, the more successful the detection rate.
Achievements
• Building a large dataset of 79 million 32x32 color labeled images.
• Showing that a simple non-parametric method, in conjunction with large dataset, can give reasonable performance on object recognition task.
• Tasks as Person detection and Scene detection perform as good as leading class specific detectors
Conclusions
It is possible to put less effort into the modeling part in object recognition (seeking to develop suitable parametric representation for recognition), while simultaneously improving the dataset itself can help to solve the same problem.
References• 80 million tiny images
– http://people.csail.mit.edu/torralba/publications/80millionImages.pdf
• ImageNet– http://wordnet.cs.princeton.edu/papers/imagenet_cvpr09.pdf
• WordNet– http://wordnet.princeton.edu/wordnet/
• Precision and recall– http://en.wikipedia.org/wiki/Precision_and_recall
• ROC curve– http://en.wikipedia.org/wiki/Receiver_operating_characteristic
• Images taken from:– http://royal.pingdom.com/– http://en.wikipedia.org/wiki/Google_Images– http://en.wikipedia.org/wiki/Content-based_image_retrieval– http:// www.prag.diee.unica.it– http:// www.dailydawdle.com– www.apartmenttherapy.com– http://www.cs.princeton.edu/courses/archive/spr07/cos226/assignments/wordnet.html
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