Video segmentation plays an integral role in many multimedia applications, such as digital libraries, content management systems, and various other video browsing, indexing, and retrieval systems. Many algorithms for segmentation of video have appeared within the past few years. Most of these algorithms perform well on cuts, but yield poor performance on gradual transitions or special effects edits. A complete video segmentation system must also achieve good performance on special effect edit detection. In this paper, we discuss the performance of our Video Trails-based algorithms, with other existing special effect edit-detection algorithms within the literature. Results from experiments testing for the ability to detect edits from TV programs, ranging from commercials to news magazine programs, including diverse special effect edits, which we have introduced.