A Genetic Algorithm for Video Segmentation and Summarization


We describe a genetic segmentation algorithm for video. This
algorithm operates on segments of a string representation. It is
similar to both classical genetic algorithms that operate on bits of
a string and genetic grouping algorithms that operate on subsets
of a set. For evaluating segmentations, we define similarity
adjacency functions, which are extremely expensive to optimize
with traditional methods. The evolutionary nature of genetic
algorithms offers a further advantage by enabling incremental
segmentation. Applications include video summarization and
indexing for browsing, plus adapting to user access patterns.