Batch Mode Active Learning for Multimedia Pattern Recognition

Multimedia applications like face recognition and facial expression recognition inherently rely on the availability of a large amount of labeled data to train a robust recognition system. In order to induce a reliable classification model for a multimedia pattern recognition application, the data is typically labeled by human experts based on some domain knowledge. However, manual annotation of a large number of images is an expensive process in terms of time, labor and human expertise. This has led to the development of active learning algorithms, which automatically identify the salient instances from a given set of unlabeled data and are effective in reducing the human annotation effort to train a classification model. Further, to address the possible presence of multiple labeling oracles, there have been efforts towards a batch form of active learning, where a set of unlabeled images are selected simultaneously for labeling instead of a single image at a time. Existing algorithms on batch mode active learning concentrate only on the development of a batch selection criterion and assume that the batch size (number of samples to be queried from an unlabeled set) to be specified in advance. However, in multimedia applications like face/facial expression recognition, it is difficult to decide on a batch size in advance because of the dynamic nature of video streams. Further, multimedia applications like facial expression recognition involve a fuzzy label space because of the imprecision and the vagueness in the class label boundaries. This necessitates a BMAL framework, for fuzzy label problems. To address these fundamental challenges, we propose two novel BMAL techniques in this work: (i) a framework for dynamic batch mode active learning, which adaptively selects the batch size and the specific instances to be queried based on the complexity of the data stream being analyzed and (ii) a BMAL algorithm for fuzzy label classification problems. To the best of our kno- ledge, this is the first attempt to develop such techniques in the active learning literature.