A Novel Dictionary Learning based Multiple Instance Learning Approach to Action Recognition from Videos

In this paper we deal with the problem of action recognition from unconstrained videos under the notion of multiple instance learning (MIL). The traditional MIL paradigm considers the data items as bags of instances with the constraint that the positive bags contain some class-specific instances whereas the negative bags consist of instances only from negative classes. A classifier is then further constructed using the bag level annotations and a distance metric between the bags. However, such an approach is not robust to outliers and is time consuming for a moderately large dataset. In contrast, we propose a dictionary learning based strategy to MIL which first identifies class-specific discriminative codewords, and then projects the bag-level instances into a probabilistic embedding space with respect to the selected codewords. This essentially generates a fixedlength vector representation of the bags which is specifically dominated by the properties of the class-specific instances. We introduce a novel exhaustive search strategy using a support vector machine classifier in order to highlight the class-specific codewords. The standard multiclass classification pipeline is followed henceforth in the new embedded feature space for the sake of action recognition. We validate the proposed framework on the challenging KTH and Weizmann datasets, and the results obtained are promising and comparable to representative techniques from the literature.