Efficient selection of informative and diverse training samples with applications in scene classification
The huge amount of time required to construct a set of labeled images to train a classifier has led researchers to develop algorithms which can identify the most informative training images, such that labelling those will be sufficient to achieve a considerable classification accuracy. In this paper we focus on choosing a subset of the most informative and diverse images based on which the classification model can be learned efficiently. The size of the subset to be chosen is determined by the available budget for manual labeling. Although the problem of identifying the informative images can be solved by active learning algorithms, it will require a set of labeled images for initial model construction, which is not required in our method as we identify the best samples at one shot. We incorporate the concepts of strong and weak teacher to help the learner to learn the model efficiently with limited budget for manual labeling. We perform rigorous experiments on two challenging scene classification datasets to demonstrate the effectiveness of our algorithm.