Multi-media Image Classification

7 important questions on Multi-media Image Classification

Core Machine Learning Components

1.   Desired output (y)?
  • Determines the task: classification, regression, etc.
2.   image representation (img > x)
  • Color histogram, Bag-of-Words
3.   Function f(x, 0)
  • SVM, k-NN, Neural Networks, Decision Tree
4.   Learning / optimization algortihm (how to set 0)
5.   Evaluation measure?
  • Classification error, Ranking error, Average precision
6.   Train data {Xi, Yi}
7.   Test set

What is attribute-based classification?

Classification using a class description in terms of semantic properties or attributes

Attribute-based classification algorithm:

1.   Provide a vocabulary of attributes
  • X, Y, Z
2.   Provide class descriptions in terms of attributes
  • Aye-ayes have properties, X, and Y, but not Z
3.   Making image attributes predictions
  • P(X|img) = 0.8
  • P(Y|img) = 0.3
  • P(Z|img) = 0.4
4.   Combine into decision: this image is not an Aye-aye
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What is k-Nearest Neighbor Classifier?

Assign an image i to the most common class among the k closest images from the training set. 

K = 1    >    find the closest image
K = 3    >    find the 3 closest images and see which image occurs more

What is the difference/similarity between k-NN and NCM?

+   Distance based classifiers


-   k-NN: assign an image to a image that is closest (point)
-   NCM: assign an image to the class mean of images (mean)

GPS is geographically unambiguous (obvious)

- Not all images are equally useful: cat vs hiking picture

- Large majority low quality: noise, low resolution

- Not use geographical info: portraits, wedding, macros


+ Geographic information: countries, regions, rivers, cities

+ Select those with GPS information

IM2GPS approach/algorithm:

- Represent each image with different features (color histogram, texture)

- Find the Nearest Neighbour(s)

- Use their location as estimate

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