Shot boundary detection via adaptive low rank and svd-updating

Usually considered as the first step in content-based video retrieval, shot boundary detection (SBD) is crucial to subsequent high-level applications like video summarization. The paper proposes an accurate video shot boundary detection method based on singular value decomposition (SVD), through the use of different mathematical theorems and interpretations. In our contribution, adaptive feature extraction is performed using the Frobenius norm of low rank approximation matrices. Each frame will be mapped into k˜-dimensional vector in the singular space according to each segment. The classification of continuity values is then achieved via double thresholding technique for detecting the hard cuts. For gradual transitions detection, the folding-in technique, known as SVD-updating, is used for the first time in video shot boundary detection. This allows for accurate detection in reduced computational time, as it is no longer necessary to recalculate the whole SVD decomposition for segment correction. Experimental results on four different datasets, including the TRECVid 2001, 2002 and 2005 SBD tasks, show the effectiveness of our detector, which outperforms recent related methods in detecting both hard and gradual transitions.