12. The non-transitory computer-readable medium or media of claim 11 further comprising: augmenting the contextual boost feature for the image window by iteratively adding an updated classification context feature to the contextual boost feature of an immediately prior iteration until a stop condition has been reached.
12. The non-transitory computer-readable medium or media of claim 11 further comprising: augmenting the contextual boost feature for the image window by iteratively adding an updated classification context feature to the contextual boost feature of an immediately prior iteration until a stop condition has been reached. 13. The non-transitory computer-readable medium or media of claim 12 wherein augmenting the contextual boost feature for the image window by iteratively adding an updated classification context feature to the contextual boost feature set of an immediately prior iteration until a stop condition has been reached comprises: [a] selecting a trained contextual boost classifier from a set of trained contextual boost classifiers; [b] applying the selected trained contextual boost classifier to the contextual boost feature set to obtain a contextual boost classifier response value; [c] responsive to the stop condition not being reached: replacing the set of image classifier response values with the contextual boost classifier response value; using the replaced set of image classifier response values to obtain the updated classification context feature set; adding the updated classification context feature set to the contextual boost feature set; selecting a next trained contextual boost classifier from the set of trained contextual boost classifier, the next trained contextual boost classifier being one that has not been previously selected; and returning to step [b]; and [d] responsive to the stop condition being reached, outputting the contextual boost classifier response value for the image window.