Alvaro Soto

DCC - PUC
Case Study
80 million tiny images: a large dataset for nonparametric object and scene recognition
A. Torralba, R. Fergus and W. T. Freeman
PAMI, Nov. 2008
Alvaro Soto
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K-Vecinos Cercanos
Predicción
Uso de métrica
de distancia
Clasificación
Votación por
mayoría
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Parámetros y Modelos
• Modelos Paramétricos
• Ej. Regresión lineal
• Modelos SemiParamétricos
• Ej. Fourier, wavelets
• Modelos No Paramétricos
• Ej. K-vecinos cercanos
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What can you do with 80M images?
A. Torralba, R. Fergus and W. T. Freeman. 80 million tiny images: a large dataset for non-parametric object and
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scene recognition. IEEE PAMI, 2006.
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How to measure similarity between images?
Patch horizontal mirror,
translations and scaling.
Individual pixel shift
in 5x5 window. T given
By best Dwarp.
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Image matching using distance metrics:
Dssd , Dwarp and Dshift
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Images
They use 7 independent image search engines: Altavista, Ask, Flickr, Cydral,
Google, Picsearch and Webshots.
They extract all non-abstract nouns from wordnet: 75.846 in total.
They automatically download all the images provided by each engine for all
non-abstract nouns.
Running over 8 months, this method gathered 97.245.098 images in total.
Once intra-word duplicates and uniform images (images with zero variance) are
removed, this number is reduced to 79.302.017 images from 75.062 words
(around 1% of the keywords had no images).
They store the images using a resolution of 32x32 pixels (eficiency).
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Wordnet
Wikipedia: WordNet es una enorme base de datos léxica del idioma inglés. Agrupa
las palabras en conjuntos de sinónimos llamados 'synsets', almacenando las relaciones
semánticas entre estos conjuntos de sinónimos.
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A histogram of images per keyword collected
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Precision in Outputs from Search Engines
Accuracy drops after the 100th image and then stabilizes at
around 44% correct on average.
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Accuracy of labeling
for different nodes
of a portion of the
Wordnet tree.
More precision
for more specific
words
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Recognition Using Wordnet and Original
Keywords Used to Retrieve Each Image
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