Column subset selection for active learning in image classification

Image classification is an important task in computer vision and machine learning. However, it is known that manually labeling images is time-consuming and expensive, but the unlabeled images are easily available. Active learning is a mechanism which tries to determine which unlabeled data points would be the most informative (i.e., improve the classifier the most) if they are labeled and used as training samples. In this paper, we introduce the idea of column subset selection, which aims to select the most representation columns from a data matrix, into active learning and propose a novel active learning algorithm, column subset selection for active learning (CSS"a"c"t"i"v"e). CSS"a"c"t"i"v"e selects the most representative images to label, then the other images are reconstructed by these labeled images. The goal of CSS"a"c"t"i"v"e is to minimize the reconstruction error. Besides, most of the previous active learning approaches are based on linear model, and hence they only consider linear functions. Therefore, they fail to discover the intrinsic geometry in images when the image space is highly nonlinear. Therefore, we provide a kernel-based column subset selection for active learning (KCSS"a"c"t"i"v"e) algorithm which performs the active learning in Reproducing Kernel Hilbert Space (RKHS) instead of the original image space to address this problem. Experimental results on Yale, AT&T and COIL20 data sets demonstrate the effectiveness of our proposed approaches.