May the torcher light our way: A negative-accelerated active learning framework for image classification

Uncertainty sampling is one of the most widely used strategy for pool-based active learning, however, there exists the problem that selected images do not reflect the desired training distribution and need additional labeling cost. To deal with this problem, from aspects of image classification and visual perception, we improve the traditional entropy-based sampling strategy by introducing bag-of-visual-words classification method and negative-accelerated learning principle from Rescorla-Wagner perceptive model. Differs from previous researches that treated sampling and classifying process separately, under the unified negative-accelerated learning model, we combine the two processes as a uniform model, named as negative-accelerated uncertainty sampling strategy with BoVW (NUSB) by proposing a new evolving sample selection measure, which takes category distribution into consideration. Classifier is trained to provide category distribution for the sampling process, reducing additional cost of annotation. Also, transfer test is utilized to prevent over-fitting and further evaluate the performance of different sampling strategies. Experimental results on real world datasets show that our active sampling framework outperforms both baseline active sampling strategies and state-of-the-art active learning based image classification method.