Method for Training Multi-Class Classifiers with Active Selection and Binary Feedback

Abstract

A multi-class classifier is trained by selecting a query image from a set of active images based on a membership probability determined by the classifier, wherein the active images are unlabeled. A sample image is selected from a set of training image based on the membership probability of the query image, wherein the training images are labeled. The query image and the sample images are displayed to a user on an output device. A response from the user is obtained with an input device, wherein the response is a yes-match or a no-match. The query image with the label of the sample image is added to the training set if the yes-match is obtained, and otherwise repeating the selecting, displaying, and obtaining steps until a predetermined number of no-match is reached to obtain the multi-class classifier.

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