The intrinsic complexity of biological systems creates huge amounts of unlabeled experimental data. The exploitation of such data can be achieved by performing active machine learning accompanied by a high-level symbolic expert who defines categories and their best boundaries using as little data as possible. We present a global strategy for designing active machine learning methods suited for the observation and analysis of complex systems, such as embryonic development. We developed a procedure that uses all available knowledge, whether gathered manually or automatically, and is able to readjust when new data is provided. We show that it is a powerful method for the investigation of the morphogenetic features of embryogenesis and specifically mitosis detection. It will make possible to properly reconstruct the in vivo cell morphodynamics, a main challenge of the post-genomic era.