A New Separable Moments Based on Tchebichef-Krawtchouk Polynomials

Idan, Zinah N.; Abdulhussain, Sadiq H.; Al-Haddad, S. A. R.

doi:10.1109/access.2020.2977305

Cited by 20 publications

(16 citation statements)

References 26 publications

(47 reference statements)

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“…These moments are used to transform the video frames into the moment domain and form the features. In this paper, STKP is utilized as an OP since it has been proven that its performance outperforms other OPs in terms of energy compaction and localization property over other existing OPs [38]. In addition, the extracted features affect the selection of the candidate transition, as shown in Section IV-B.…”

Section: Preliminariesmentioning

confidence: 99%

“…Subsequently, 1D and 2D signals are described by the moments. For a two dimensional (2D) signal I(x, y) with a size of N × N , the moment can be defined as [38]:…”

Section: B Moment Computationmentioning

confidence: 99%

“…The matrix representation is used to provide a second faster and simplified form of the moment in Equation (4) as follows [38]:…”

Section: B Moment Computationmentioning

confidence: 99%

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Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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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%.

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Section: Preliminariesmentioning

confidence: 99%

“…Subsequently, 1D and 2D signals are described by the moments. For a two dimensional (2D) signal I(x, y) with a size of N × N , the moment can be defined as [38]:…”

Section: B Moment Computationmentioning

confidence: 99%

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Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

et al. 2021

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“…Legendre moments belong to continuous orthogonal moments. Orthogonal moments are widely used as a shape descriptor because they alleviate information redundancy and can reconstruct signals (Idan et al, 2020). Continuous orthogonal moments are computationally complex, which increases the overall retrieval time.…”

Section: Comparison Among the State-of The Art Approachesmentioning

confidence: 99%

Content-based image retrieval: A review of recent trends

2021

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With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

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“…If k = 2 (shown in Figure 9c), (even number) it is difficult to classify the object because of achieving the same score of two classes labels. Orthogonal moments face recognition [143], object classification [144], [145], object recognition [128], [146], texture retrieval [147] • strong signal descriptors with low order elements [129] • computationally expensive [129] Krawtchouk polynomials object recognition [130], edge detection [148], object classification [149], image recognition [150] • better performance for reconstruction error [23], [130] • high computational time [23] Tchebichef polynomials image analysis [131], face Recognition [151], edge detection [132], image retrieval [152] • eliminates the necessity for numerical approximation in the image discrete domain [131], [133] • vulnerable coefficients' calculation to numerical instability for higher polynomial order [132] Charlier polynomials object recognition [153], image classification [154], image reconstruction [155], object recognition [156] • minimizes both the time of computation and the error of propagation [136] • coefficient's numerical inconsistency for higher-order polynomials [136] SKTP face detection [140] • stable in noisy environments [140] • no clear information for handling major occlusion [140] B. DECISION TREE Decision trees are developed for classification tasks.…”

Section: Classifiers a Nearest Neighbour Classifiermentioning

confidence: 99%