Incremental learning in automatic target recognition

Chaitanya Raju; Karthik Mahesh Varadarajan; Aditya Kothari; Hieu Tat Nguyen; Jacob Yadegar; Jonathan Mills

doi:10.1117/12.819949

4 May 2009 Incremental learning in automatic target recognition

Chaitanya Raju, Karthik Mahesh Varadarajan, Aditya Kothari, Hieu Tat Nguyen, Jacob Yadegar, Jonathan Mills

Proceedings Volume 7335, Automatic Target Recognition XIX; 73350X (2009) https://doi.org/10.1117/12.819949
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

ATR in two dimensional images is valuable for precision guidance, battlefield awareness and surveillance applications. Current ATR methods are largely data-driven and as a result, their recognition accuracy relies on the quality of training dataset. These methods fail to reliably recognize new target types and targets in new backgrounds and/or atmospheric conditions. Thus, there is a need for an ATR solution that can constantly update itself with information from new data samples (samples may belong to existing classes, background clutter or new target classes). In the paper, this problem is addressed in two steps: 1) Incremental learning with Fully Adaptive Approximate Nearest Neighbor Classifier (FAAN) - A novel data structure is designed to allow incremental learning in approximate nearest neighbor classifier. New data samples are assimilated at reduced complexity and memory without retraining on existing data samples, 2) Data Categorization using Data Effectiveness Measure (DEM) - DEM of a data sample is a degree to which each sample belongs to a local cluster of samples. During incremental learning, DEM is used to filter out redundant samples and outliers, thereby reducing computational complexity and avoiding data imbalance issues. The performance of FAAN is compared with proprietary Bagging-based Incremental Decision Tree (ABAFOR) implementation. Tests performed on Army ATR database with over 37,000 samples shows that while classification accuracy of FAAN is comparable to ABAFOR (both close to 95%), the process of incremental learning is significantly quicker.

Citation Download Citation

Chaitanya Raju, Karthik Mahesh Varadarajan, Aditya Kothari, Hieu Tat Nguyen, Jacob Yadegar, and Jonathan Mills "Incremental learning in automatic target recognition", Proc. SPIE 7335, Automatic Target Recognition XIX, 73350X (4 May 2009); https://doi.org/10.1117/12.819949

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