Multiple Grey-scale Feature Based Shot Boundary Detection

Jose, Jasmin T.; Rajkumar, S.

doi:10.1109/asiancon51346.2021.9544096

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…)e use of ECR and CLD features has great significance in the SBD process. To prove this, we have compared the proposed method with our multiple grey scale featuresbased SBD method [26]. Gray level cooccurrence matrix (GLCM), histogram, and SIFT key point matching are used as features in [26].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…To prove this, we have compared the proposed method with our multiple grey scale featuresbased SBD method [26]. Gray level cooccurrence matrix (GLCM), histogram, and SIFT key point matching are used as features in [26]. Table 5 shows the comparison results.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…A comparison of our experimental results with existing methods reveals that the proposed method outperforms in SBD process by reducing the false positives. Some basic materials of this paper have been presented at a 2021 event [26], but the readers should consider the current version as our final work.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Shot Boundary Detection with Multiple Visual Representations

Jose

Rajkumar

Ghalib³

et al. 2022

Mobile Information Systems

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Due to the unlimited growth of video-capturing devices and media, searching and finding a particular video in this huge database becomes a laborious as well as expensive task. Information-rich shots are the inevitable factor of the content-based video processing (CBVP) system. Hence, shot boundary detection (SBD) becomes the basic step of all content-based video retrieval processes. The accuracy of the existing SBD methods highly suffers from false positives and false negatives due to the presence of multiple variants. An efficient SBD method with multiple invariant features is proposed in this paper. A right combination of invariant features such as edge change ratio (ECR), colour layout descriptor (CLD), and scale-invariant feature transform (SIFT) key point descriptors helped to improve the accuracy level of SBD. As the selected features are invariant to most of the variants in video frames, such as illuminance changes, motion, scaling, and rotation, a markable reduction in false detection is possible. Support vector machine (SVM) classifier is used for the classification of frames into transition frames and shot frames. This proposed method is experimented and analysed with the standard SBD dataset TRECVid 2007 videos. The experimental results are compared with some state-of-art methods, and our method shows better performance with a 97% of F1 score.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%