In many machine-learning applications, each data point can be represented as a set of instances that create multiple instance learning (MIL) problems. Due to the structure of images, different regions can be interpreted as instances. Thus, multiple instances can be obtained for each image, which makes image categorization a MIL problem. With abundant unlabeled image data, this MIL problem can be solved using active learning algorithms. Active learning is a framework that utilizes unlabeled data in which labeling samples is a labor-intensive and expensive task. Although many effective MIL active learning methods have been developed, most of the existing algorithms do not take into account classifier and feature representation. In this work, we develop DEMIAL (dictionary ensembles multiple instance active learning), a multiple instance active learning method that utilizes sparse feature representation and classifier ensemble techniques. In the proposed active learning framework, we employ dictionary learning and compare uncertainty- and entropy-based instance selection techniques. Experimental results show that classifier ensembles benefit from active learning and the DEMIAL algorithm outperforms the kernel-based multiple instance active learning framework.