Long transition analysis for post shot boundary detection With the introduction of the standard in video indexing, research in shot boundary detection has recently become popular. While new solutions for long transition detection have recently emerged, there is not much literature that focuses on what to do with the frames within the transition when they are detected. After the approximate position of the long transition is detected, the raw sequence cannot be used for segmentation and indexing due to the special effects incorporated. Here, a technique aims at extracting extra information from the transition after the existence of a long transition is confirmed. This technique consists of four stages, namely, shot boundary refinement, shot type determination, frame reconstruction for soft transition and pixel classification for hard transition. This paper gives the overview as well as performance of each stage. This technique allows detected transitions to be analysed without human intervention.

Key frame extraction from unstructured consumer video clips We present a key frame extraction method dedicated to summarize unstructured consumer video clips acquired from digital cameras. Analysis of spatiotemporal changes over time provides meaningful information about the scene and the cameramanu0027s general intents. First, camera and object motion are estimated and used to derive motion descriptors. A video is segmented into homogeneous segments based on major types of camera motion (e.g., pan, zoom, pause, steady). Dedicated rules are used to extract candidate key frames from each segment. Confidence measures are computed for the candidates to enable ranking in semantic relevance. This method is scalable so that we can produce any desired number of key frames from the candidates. We demonstrated the effectiveness of our method by comparing results with the ground truth agreed by multiple judges.