The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.
Orthogonal moments are beneficial tools for analyzing and representing images and objects. Different hybrid forms, which are first and second levels of combination, have been created from the Tchebichef and Krawtchouk polynomials. In this study, all the hybrid forms, including the first and second levels of combination that satisfy the localization and energy compaction (EC) properties, are investigated. A new hybrid polynomial termed as squared Tchebichef-Krawtchouk polynomial (STKP) is also proposed. The mathematical and theoretical expressions of STKP are introduced, and the performance of the STKP is evaluated and compared with other hybrid forms. Results show that the STKP outperforms the existing hybrid polynomials in terms of EC and localization properties. Image reconstruction analysis is performed to demonstrate the ability of STKP in actual images; a comparative evaluation is also applied with Charlier and Meixner polynomials in terms of normalized mean square error. Moreover, an object recognition task is performed to verify the promising abilities of STKP as a feature extraction tool. A correct recognition percentage shows the robustness of the proposed polynomial in object recognition by providing a reliable feature vector for the classification process.
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