A genetic segmentation algorithm for image data streams and video
Patrick Chiu, Eleanor Gilbert Rieffel, Andreas Girgensohn, Lynn D. Wilcox, Wolf Polak, Forrest H Bennett · 2000
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. 1 INTRODUCTION Segmenting multimedia data streams is a fundamental problem with many applications. By segmentation, we mean breaking up a data stream into meaningful parts. Properly segmented streams can be better organized and reused. They provid...