Abstract
With the increasing growth of multimedia data, the current real-world video sharing websites are being huge in repository size, more specifically video databases. This growth necessitates to look for superior techniques in processing video because video contains a lot of useful information. Temporal video segmentation (TVS) is considered essential stage in content-based video indexing and retrieval system. TVS aims to detect boundaries between successive video shots. TVS algorithm design is still challenging because most of the recent methods are unable to achieve fast and robust detection. In this regard, this paper proposes a TVS algorithm with high precision and recall values, and low computation cost for detecting different types of video transitions. The proposed algorithm is based on orthogonal moments which are considered as features to detect transitions. To increase the speed of the TVS algorithm as well as the accuracy, fast block processing and embedded orthogonal polynomial algorithms are utilized to extract features. This utilization will lead to extract multiple local features with low computational cost. Support vector machine (SVM) classifier is used to detect transitions. Specifically, the hard transitions are detected by the trained SVM model. The proposed algorithm has been evaluated on four datasets. In addition, the performance of the proposed algorithm is compared to several state-of-the-art TVS algorithms. Experimental results demonstrated that the proposed algorithm performance improvements in terms of recall, precision, and F1-score are within the ranges (1.31 - 2.58), (1.53 - 4.28), and (1.41 - 3.03), respectively. Moreover, the proposed method shows low computation cost which is 2% of real-time.
Highlights
The immense growth of computer performances and the low cost of storage devices during the past decades led to the dominance of multimedia data in the cyberspace, rise in the volume of transmitted data, and the size of repositories [1]
This work has different stages, where each stage has a significant impact on the performance of the proposed Temporal video segmentation (TVS) algorithm
moments of smoothed images (MoSI) and moments of gradient images (MoGI) are used to reduce the effect of disturbance factors such as noise, object motion, camera motion, and flash lights
Summary
The immense growth of computer performances and the low cost of storage devices during the past decades led to the dominance of multimedia data in the cyberspace, rise in the volume of transmitted data, and the size of repositories [1]. TVS algorithms are primarily centered on the uncompressed domain, for instance, pixel-based algorithms [19] They are developed to encompasses other approaches such as: edge information [20], histogram of video frames [21], transform coefficients [22], and local keypoint [1]. Several researchers employed the coefficients of discrete transforms as a feature extraction tool, such as discrete Fourier transform, discrete Wavelet transform, and discrete Walsh-Hadamard transform These methods exhibits a good performance in detecting video shot transitions [23]; their computational cost is considered high [17]. Improvement in terms of the detection accuracy for HTs and STs is still demanded [17] Motivated by these issues, this paper proposes a fast and accurate TVS algorithm based on discrete orthogonal moments.
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