Rapid training of image classifiers through adaptive, multi-frame sampling method

Abstract

Computer vision methods, such as automatic target recognition (ATR) techniques, have the potential to improve the accuracy of military systems for weapon deployment and targeting, resulting in greater utility and reduced collateral damage. A major challenge, however, is training the ATR algorithm to the specific environment and mission. Because of the wide range of operating conditions encountered in practice, advanced training based on a pre-selected training set may not provide the robust performance needed. Training on a mission-specific image set is a promising approach, but requires rapid selection of a small, but highly representative training set to support time-critical operations. To remedy these problems and make short-notice seeker missions a reality, we developed Learning and Mining using Bagged Augmented Decision Trees (LAMBAST). LAMBAST examines large databases and extracts sparse, representative subsets of target and clutter samples of interest. For data mining, LAMBAST uses a variant of decision trees, called random decision trees (RDTs). This approach guards against overfitting and can incorporate novel, missionspecific data after initial training via perpetual learning. We augment these trees with a distribution modeling component that eliminates redundant information, ignores misrepresentative class distributions in the database, and stops training when decision boundaries are sufficiently sampled. These augmented random decision trees enable fast investigation of multiple images to train a reliable, mission-specific ATR. This paper presents the augmented random decision tree framework, develops the sampling procedure for efficient construction of the sample, and illustrates the procedure using relevant examples.