1. A method for training and using an object classifier to identify a class object from a captured image, the method comprising the steps of: (a) obtaining a plurality of still images from training data; (b) applying a feature generation technique to the plurality of still images of the training data for identifying candidate features from each respective image, wherein applying the feature generation technique further comprises the steps of: identifying a comparative image of a known object; randomly extracting a fragment from the comparative image; comparing the extracted fragment to each of the plurality of still images, wherein each still image is partitioned into a plurality of image sections that are identified by an image position, wherein the fragment and each image position are compared using a feature descriptor technique; determining a similarity score between the fragment and each image position in each image; identifying the respective image position having a highest similarity score in each respective image; compiling a group of fragments that have the highest similarity score from each respective image; (c) selecting a subset of features from the candidate features using a similarity comparison technique; (d) iteratively repeating steps (a) through (c) a predetermined number of times as a function of the selected subset of features identified in step (c); (e) generating a trained object classifier; (f) capturing an image from an image capture device; (g) classifying features in the captured image using the trained object classifier; and (h) determining whether the captured image contains a class object based on the trained object classifier associating an identified feature in the image with the class object.
1. A method for training and using an object classifier to identify a class object from a captured image, the method comprising the steps of: (a) obtaining a plurality of still images from training data; (b) applying a feature generation technique to the plurality of still images of the training data for identifying candidate features from each respective image, wherein applying the feature generation technique further comprises the steps of: identifying a comparative image of a known object; randomly extracting a fragment from the comparative image; comparing the extracted fragment to each of the plurality of still images, wherein each still image is partitioned into a plurality of image sections that are identified by an image position, wherein the fragment and each image position are compared using a feature descriptor technique; determining a similarity score between the fragment and each image position in each image; identifying the respective image position having a highest similarity score in each respective image; compiling a group of fragments that have the highest similarity score from each respective image; (c) selecting a subset of features from the candidate features using a similarity comparison technique; (d) iteratively repeating steps (a) through (c) a predetermined number of times as a function of the selected subset of features identified in step (c); (e) generating a trained object classifier; (f) capturing an image from an image capture device; (g) classifying features in the captured image using the trained object classifier; and (h) determining whether the captured image contains a class object based on the trained object classifier associating an identified feature in the image with the class object. 11. The method of claim 1 wherein a score is generated that identifies the similarity between the appearance descriptors of each extracted fragment of the comparative image and each image location in each still image.