1. A computer-implemented method, comprising: applying a plurality of view-based classifiers to a digital image, wherein each classifier corresponds to a respective portion of the digital image and is configured to determine whether at least a portion of a type of object is within the respective portion of the digital image to which the classifier is applied; computing, based on the applying, a sum of a plurality of log-likelihood ratios for the plurality of view-based classifiers, each log-likelihood ratio of the plurality of log-likelihood ratios being for a respective classifier of the plurality of view-based classifiers and including a ratio of two graphical probability models, a graphical probability model including a probability distribution over a set of variables where statistical independence and conditional statistical independence exist among various combinations of the variables, and wherein the graphical probability model is a probability distribution representation derived from statistical dependencies among image input variables; determining that the type of object is within the digital image based on the sum satisfying a first predetermined threshold; identifying a detection location of the type of object within the digital image based on respective locations within the digital image to which the plurality of classifiers were applied and the plurality of log-likelihood ratios for the plurality of classifiers; and displaying a marked-up version of the digital image identifying the identified detection location of the type of object within the digital image.