Shot-boundary detection: unraveled and resolved?

Partitioning a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A video shot is defined as a series of interrelated consecutive frames taken contiguously by a single camera and representing a continuous action in time and space. As such, shots are considered to be the primitives for higher level content analysis, indexing, and classification. The objective of this paper is twofold. First, we analyze the shot-boundary detection problem in detail and identify major issues that need to be considered in order to solve this problem successfully. Then, we present a conceptual solution to the shot-boundary detection problem in which all issues identified in the previous step are considered. This solution is provided in the form of a statistical detector that is based on minimization of the average detection-error probability. We model the required statistical functions using a robust metric for visual content discontinuities (based on motion compensation) and take into account all (a priori) knowledge that we found relevant to shot-boundary detection. This knowledge includes the shot-length distribution, visual discontinuity patterns at shot boundaries, and characteristic temporal changes of visual features around a boundary. Major advantages of the proposed detector are its robust and sequence-independent performance, while there is also the possibility to detect different types of shot boundaries simultaneously. We demonstrate the performance of our detector regarding two most widely used types of shot boundaries: hard cuts and dissolves