Histogram Based Split and Merge Framework for Shot Boundary Detection

Abstract

Abstract. In this paper, we propose a non-parametric approach for shot boundary detection in videos. The proposed method exploits the split and merge framework by the use of color histograms. Initially, every frame of the input video sequence undergoes color quantization and subsequently, the color histograms are computed for every quantized frame. The split and merge is driven by the fishers linear discriminant criterion function which results with a set of subsequences after several iterations which are assumed to be the shots present in the given video. The proposed method is experimentally tested on video samples from TrecVid 2002 dataset and YouTube online database. We have obtained overall accuracy of 85.5% Precision, 87.1% Recall and 86.1% Fmeasure for the dataset used. A comparative study of the proposed approach with the contemporary research works is also carried out.

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

“…The idea behind histogram-based approaches ( [7], [8]) is that two frames with unchanging background and unchanging (although moving) objects will have little difference in their histograms. Color histograms are used in [9] to detect shot boundaries by representing each frame of the video by their color histogram features. Then, the video frames are treated as a sequence of feature vectors which are fed to the split and merge framework.…”

Video Shot Boundary Detection in Sport Video Using the Scale Invariant Feature Transform

“…A step further towards reducing sensitivity to camera and object movements can be done by comparing the histograms of successive images. The idea behind histogram-based approaches [7][8] is that two frames with unchanging background and unchanging (although moving) objects will have little difference in their histograms. In [7] a histogram-based is used as a second detector where the information of video transition is used to validate or invalidate the ambiguous scene cut declared by the first detector which is the DC image difference.…”

“…Several approaches are fused with different combinations to make use of the advantages of various popular techniques. Various features such as color, texture, shape, SIFT, motion vectors, edges in spatial as well as in transformed domains such as Fourier, cosine wavelets, Eigen values, etc…, are used majorly with different combinations of the same in many popular approaches [8].…”