Modelling of content-aware indicators for effective determination of shot boundaries in compressed MPEG videos

Abstract

Abstract. In this paper, a content-aware approach is proposed to design multiple test conditions for shot cut detection, which are organized into a multiple phase decision tree for abrupt cut detection and a finite state machine for dissolve detection. In comparison with existing approaches, our algorithm is characterized with two categories of content difference indicators and testing. While the first category indicates the content changes that are directly used for shot cut detection, the second category indicates the contexts under which the content change occurs. As a result,indications of frame differences are tested with context awareness to make the detection of shot cuts adaptive to both content and context changes. Evaluations announced by TRECVID 2007 indicate that our proposed algorithm achieved comparable performance to those using machine learning approaches, yet using a simpler feature set and straightforward design strategies. This has validated the effectiveness of modelling of content-aware indicators for decision making, which also provides a good alternative to conventional approaches in this topic.

Methods and Challenges in Shot Boundary Detection: A Review

“…In this method, the transition center is identified by considering different frame steps. The proposed method iteratively measures the linearity behavior of a transition by minimizing the error function [ 151 ]. Although an adaptive threshold is implemented in the preprocessing stage, the results are not always satisfactory [ 15 ].…”

“…Moreover, the computational cost is high [ 38 ]. Owing to the algorithm assumption that features within shots are constant, the algorithm suffers from camera/object motions [ 151 , 152 ].…”

Fast Video Shot Boundary Detection Technique based on Stochastic Model

“…For a manageable video database, only key frames are indexed and hence video data retrieval systems process queries based on a similarity measure between the query input and the key frames data. Several techniques have been developed in recent years to summarize video sequences (Chen et al, 2008;Jiang, Sun, Liu, Chao, & Zhang, 2013;Chen, Ren, & Jiang, 2011;Jiang, Sun, Liu, Chao, & Zhang, 2013). These techniques vary in their performance and complexity.…”

“…Authors' experiments showed that Tsallis mutual information similarity measure is the best when using HSV and Lab color spaces. Chen, Ren, and Jiang (2011) employed content aware approach to detect cut and dissolve bounderies. For cut detection they utilized content changes in multiple phase decision tree.…”

Histogram Based Split and Merge Framework for Shot Boundary Detection

“…Some of the major approaches are: histograms based approaches [10][11] where, the similarities and continuity of the frames in a sequence are measured with the help of differences of histograms to arrive at the possible locations of maximum discontinuities, block based approaches [12] where each video frame is studied at the block level to extract local features and matched with the corresponding blocks of the subsequent frames for the identification of shot change, model based approaches [13][14] where a model is trained to identify the possible shots, cluster based approaches [15][16][17] where frame sequence is clustered into several clusters and every cluster is checked for the possibility of being a shot, non-parametric approaches [18] where shot boundaries are detected without consuming any parameters such as a threshold, compressed domain approaches [19][20][21] where the video is processed in its compressed domain itself so that, the time of decompression is avoided, fusion based approaches [22][23][24] where, several approaches are fused with different combinations to make use of the advantages of various popular techniques and so on. Various features such as color, texture, shape, sketch, 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.…”