Abstract: Computerized techniques are provided for storing a set of representative features for use with a machine learning process. A first set of training images for a class are accessed and processed, using a feature extraction technique, to determine a set of features for the class. A set of representative features from the set of features for the class is determined using a selection technique. The set of representative features are stored for the class. A second set of images is accessed. The machine learning process is executed, using the stored set of representative features, including executing one or more of: a training phase to train a classifier using the second set of images and at least part of the stored set of representative features; and a testing phase to run the classifier using the second set of images and at least part of the stored set of representative features.

Abstract: The techniques described herein relate to computerized methods and apparatus for grouping images of objects based on semantic and visual information associated with the objects. The techniques described herein further relate to computerized methods and apparatus for training a machine learning model for object recognition.

Abstract: A query is submitted to a search engine, where the query includes an identification of a symbol. A bounding box is generated in an unlabeled image returned by the search engine in response to the query. A confidence score is also generated that indicates a likelihood of the symbol being present in a portion of the unlabeled image enclosed by the bounding box. The unlabeled image is selected as a training image for training a system to recognize the symbol, when the confidence score is above a predefined threshold.