Video Segmentation and Structuring for Indexing Applications

Abstract

This paper introduces a complete framework for temporal video segmentation. First, a computationally efficient shot extraction method is introduced, which adopts the normalized graph partition approach, enriched with a non-linear, multiresolution filtering of the similarity vectors involved. The shot boundary detection technique proposed yields high precision (90%) and recall (95%) rates, for all types of transitions, both abrupt and gradual. Next, for each detected shot, the authors construct a static storyboard by introducing a leap keyframe extraction method. The video abstraction algorithm is 23% faster than existing techniques for similar performances. Finally, the authors propose a shot grouping strategy that iteratively clusters visually similar shots under a set of temporal constraints. Two different types of visual features are exploited: HSV color histograms and interest points. In both cases, the precision and recall rates present average performances of 86%.

Methods and Challenges in Shot Boundary Detection: A Review

“…Histograms are considered substitutes for PBAs because they do not consider the spatial information of all color spaces. Hence, histograms, to some extent, are regarded as invariant to local motion or small global motion compared with PBAs [ 75 , 76 ].…”

Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels

“…Only the luminance channel of the considered videos is considered in this case. If the number of pixels which change from one image to another exceeds a certain threshold a shot transition is declared [4]. A technique introduced and validated during the TRECVID 2004 campaign is presented in [5].…”