SC1015. Understanding and Interpreting Images.

SC1015. Understanding and Interpreting Images.
23-Jan-2011

Electronic Imaging Conference. 23-27 Jan, 2011.

Edward Adelson
http://web.mit.edu/persci/people/adelson/

“Pictures are not taken in a vacuum--an overview of exploiting context for semantic scene content understanding” Signal Processing Magazine, IEEE
March 2006, Vol23 Issue 2.

Main subject detection. Belief map.

Duda, Hurt, and Storic. Statistical Pattern Recognition
http://amzn.com/0471056693

Earth Mover Distance
http://en.wikipedia.org/wiki/Earth_mover%27s_distance

Mahalonobis Distance
http://en.wikipedia.org/wiki/Mahalanobis_distance

PCA - Principle Component Analysis
http://en.wikipedia.org/wiki/Principal_component_analysis

SURF : Speed-up Robust Features (came after SIFT)
http://en.wikipedia.org/wiki/SURF

“A computational approach to determination of main subject regions in photographic images”
http://dx.doi.org/10.1016/j.imavis.2003.09.012

Integral Image
http://en.wikipedia.org/wiki/Summed_area_table

Haar Filters
http://en.wikipedia.org/wiki/Haar-like_features

Gradient Field.
http://en.wikipedia.org/wiki/Gradient

libsvm Support Vector Machine library
http://www.csie.ntu.edu.tw/~cjlin/libsvm/

AdaBoost
http://en.wikipedia.org/wiki/AdaBoost

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
http://www.cs.waikato.ac.nz/ml/weka/

AndreaMosaic - photo mosaic software (Windows)