Patent ID: 8842883

Claim:
A computer-implemented method for training a detection system for detecting an object in an image, the method comprising: obtaining a set of image patches, wherein each image patch is associated with a label indicating whether or not the image patch contains the object and with at least one feature; using the features of the set of image patches to train a global classifier; applying the global classifier to the features of the set of image patches to identify a set of ambiguous features that have response values from the global classifier that are within an ambiguous response value range; selecting a number of clusters to cluster the set of ambiguous features based at least in part on detection accuracy of classifiers trained using different clustering; clustering the set of ambiguous features using the selected number of clusters; and for each cluster, using the ambiguous features associated with the cluster and the labels associated with those features to train a local classifier for that cluster; wherein the step of selecting a number of clusters to cluster the set of ambiguous features based at least in part on detection accuracy of classifiers trained using different clustering, comprises: separating the set of ambiguous features into a training set and a validation set; computing eigenvalues using an affinity matrix formed using the training set; identify eigenvalues for generating a set of candidate pairs of spectral clustering parameters α and β; for each candidate pair of spectral clustering parameters α and β: using the candidate pair of parameters α and β to cluster a training set; for each cluster, using the ambiguous features associated with the cluster and the labels associated with those ambiguous features to train a local classifier for that cluster; and evaluating the local classifiers using the validation set and compute an error value; and selecting the candidate pair of parameters α and β for the training set associated with the error value that is the lowest.