Abstract: Systems and methods for selecting at least one unlabeled data object from a set of unlabeled data objects. The present invention receives a set of unlabeled data objects and identifies at least one data object in the set that is considered to differ from the others. The at least one data object is selected for further processing, which may include labeling processes. In some embodiments, the data objects are passed through at least one representation-generating module, and the resulting representations are compared to each other. Differences between the representations are evaluated against at least one criterion. If the differences meet the at least one criterion, corresponding data objects are considered to differ from the others and are then selected for further processing. In some implementations, a sample set of sample data objects may be used. In some implementations, the at least one representation-generating module may comprise a neural network.

Abstract: System and methods for associating unlabeled images with other, labeled images of the same locations. A high-level signature of each image is generated, representing high-level structural features of each image. A signature of an unlabeled image is then compared to a signature of a labeled image. If those signatures match within a margin of tolerance, the images are interpreted as representing the same location. One or more labels from the labeled image can then be automatically applied to the unlabeled image. In one embodiment, the images are frames from separate video sequences. In this embodiment, entire unlabeled video sequences can be labeled based on a labeled video sequence covering the same geographic area. In some implementations, the high-level signatures are generated by rule-based signature-generation modules. In other implementations, the signature-generation module can be a neural network, such as a convolutional neural network.

Abstract: Systems and methods for automatic labeling of unlabeled video frames from a video sequence, based on known features in other frames in the sequence. An unlabeled video frame and a labeled video frame are received by an identification module. The unlabeled frame and the labeled video frame are temporally close to each other within the video sequence and preferably temporally adjacent. The identification module recognizes labeled features within the labeled frame. The identification module then identifies multiple potential features within the unlabeled frame. A comparison module then compares each potential feature in the unlabeled frame to the recognized labeled feature in the labeled frame. If a match is found, a labeling module applies a label to the potential feature in the unlabeled frame, thereby producing a newly labeled frame. The labeling process repeats until all frames in the sequence have been labeled.