Learning From Unstructured Multimedia Data

Information in today's world is highly heterogeneous and unstructured. Learning and inferring from such data is challenging and is an active research topic. In this paper, we present and investigate an approach to learning from heterogeneous and unstructured multimedia data. Inspired by approaches in many fields including computer vision, we investigate a histogram based approach to represent multimodal unstructured data. While existing works have predominantly focused on histogram based approaches for unimodal data, we present a methodology to represent unstructured multimodal data. We explain how to discover the prototypical features or codewords over which these histograms are built. We present experimental results on classification and retrieval tasks performed on the histogram based representation.