From video pornography to cancer cells: a tensor framework for spatiotemporal description

Spatiotemporal description is a research field with applications in various areas such as video indexing, surveillance, human-computer interfaces, among others. Big Data problems in large databases are now being treated with Deep Learning tools, however we still have room for improvement in spatiotemporal handcraft description. Moreover, we still have problems that involve small data in which data augmentation and other techniques are not valid, or even, it is not worth the use of an expensive method. The main contribution of this work is the development of a framework for spatiotemporal representation using orientation tensors enabling dimension reduction and invariance. This is a multipurpose framework called Features As Spatiotemporal Tensors (FASTensor). We evaluate this framework in two different applications: Video Pornography classification and Cancer Cell classification. The latter one is also a contribution of this work, since we introduce a new dataset called Melanoma Cancer Cell (MCC). It is a small dataset with inherent difficulties in the acquisition process and its particular motion nature. The results are competitive, while also being ease to compute. Finally, our results in the MCC dataset can be used in other cancer cell treatment analysis.