A Genetic Segmentation Algorithm for Image Data Streams and Video.

Abstract

We describe a genetic segmentation algorithm for image data streams and 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. It employs a segment fair crossover operation. 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 browsing and summarizing video and collections of visually rich documents, plus a way of adapting to user access patterns.