Parameter Analysis of the Jensen-Shannon Divergence for Shot Boundary Detection in Streaming Media Applications

Abstract

Shot boundary detection is an integral part of multimedia, be it video management or video processing. Multiple boundary detection techniques have been developed throughout the years, but are only applicable to very specific instances. The Jensen-Shannon divergence (JSD) is one such a technique that can be implemented to detect the shot boundaries in digital videos. This paper investigates the use of the JSD algorithm to detect shot boundaries in streaming media applications. Furthermore, the effects of the various parameters used by the JSD technique, on the accuracy of the detected boundaries, are quantified by the recall and precision metrics all the while keeping track of how they affect the execution time.

Video Shot Boundary Detection Based on Feature Fusion and Clustering Technique

“…In the application, feature detection and extraction usually can be divided into two directions based on processing compressed video domain and non-compressed video domain. The non-compressed domain method refers to the algorithms based on visual features, such as histogram [4] [9][10] [11] [12], pixel [13] [14] [15] [16], edge shape [17], motion [18] as well as orthogonal polynomial [19] [20] [21] [22]. While the compressed domain method refers to the algorithms based on compression coding, such as entropy coding including discrete cosine transform (DCT) and discrete Fourier transform (DFT) [12], macroblock coding [23], motion vector coding [24].…”

“…For the algorithms of shot boundary detection by feature distance similarity calculation, some researches adopt the method of adaptive threshold [9][14] [28], but in many other cases, the method of threshold setting is also introduced [15]. De Klerk et al [15] propose the algorithm of shot boundary detection by threshold setting according to the calculation of the pixel value differences using Jensen Shannon divergence (JSD). In the field of shot boundary detection, there still exists difficulties in choosing suitable thresholds for different videos, and empirical thresholds usually lead to low precision [29].…”