Segmentation supervised

Although supervised and unsupervised methods have been in the past well-separated families, a number of hybrid approaches have appeared that combine both unsupervised and supervised classifiers for image segmentation purposes [61]. 

SIMCA is a supervised pattern recognition technique, which needs to have the data classrhed manually or done using HCA. SIMCA then performs PCA on each class with a sufficient number of factors retained to account for most of the variation within classes. The number of factors retained is very important. If too few are selected, the information in the model set can become distorted. By using a procedure called cross validation, segments of the data are omitted during PCA, and the omitted data are predicted and compared to the actual value. This is repeated for every data element until each point has been excluded once from the determination. The PCA model that yields the minimum prediction error for the omitted data is retained. 

Chen H, Qi F, Zhang S (2003) Supervised video object segmentation using a small number of interactions. In IEEE international conference on acoustics, speech, and signal processing, (ICASSP 03), vol 3(3). pp 365-368... 

Supervised segmentation methods work defining the different pixel classes beforehand with a series of well-identified pixels. Each different class is described by a model and these models are used to assign unknown pixels to the predefined classes. Reference pixels in and out of the classes can be selected by applying class membership masks in the image score scatter plots [74, 75], for instance, or by using results from unsupervised segmentation methods. 

Segl K, Kaufinann H (2001) Detectirai of small objects fi om high resolution panchromatic satellite imagery based on supervised image segmentation. 1EF.E Trans Geosci Remote Sens 39(9) 2080-2083... 

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