Summarization techniques can be applied to non-text data in order to perform classification and clustering of important imaging, video and other document-associated but non-text content. The advantage to this approach is that there is a multiplicity of inexpensive (even free) summarization engines, and so a robust solution can be crafted with relatively modest effort. In this paper, we present the applicability of this approach to video and imaging data, in addition to broader binary and genetic data.