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* Quick guide:
*

To run active basis baseline experiments: go to folder "sigmoid" and follow the README file there.

To run active basis experiments with background adaption, go to folder "sigmoidAdaptive".

To run adaboost experiments on locally-maximized Gabor responses, go to folder "adaboostMaxC".

To run PCA experiments, go to folder "pca".

A separate README file is attached under each folder.

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* System requirement:
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- >= 3GB memory.

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* Utilities:
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- splitData.m: random split the data for the cross validation. For a complete reproducability, we attach the splitted data files are under "RandomSplits/".

(The following three functions should be run *after* the cross validation experiments are finished.)

- analysisCrossValidation: generate plots for performance comparisons.

- displayTemplateAndMatching: display adaboost and active basis templates, and matched templates on testing images.

To get a quick glance of cross-validation experiments, please load the corresponding .mat file generated under
the same folder, e.g. "sigmoid/". The .mat file contains the ROC curve, average precision, etc.

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* Time efficiency:
*

On an Intel Core2 Dual T6600 2.4GHz machine,

- The adaboost experiment usually takes 6 ~ 12 minutes per training-testing run.

- The active basis experiment usually takes within 2 minutes per training-testing run.
  The training is particularly fast, since it only goes over positive examples.

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* For developer:
*

- "posAll/" contains all the positive examples, "negAll/" contains random natural images.

- "RandomSplits/" contains .mat files which store randomized seeds for cross validation.

- "pcaM1/" contains the code for PCA on locally maximized and upperbounded Gabor maps.