Analysis of Shot Boundary Based on Color and Texture Features of Frame

Lin, Chuen-Horng; Hsiao, Muh-Don; Fu, Li-Jung

doi:10.1088/1757-899x/53/1/012004

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Presently, so many researchers were approaching SBD with multiple features, a combination of features. Tippayya et al [6] and Lin et al [11] used multiple features. In the first one, a histogram with SURF is used and later, and a histogram as a colour feature and a histogram of gradient as a texture feature were used.…”

Section: Literature Reviewmentioning

confidence: 99%

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: Literature Reviewmentioning

confidence: 99%

Efficient Shot Boundary Detection with Multiple Visual Representations

Jose

Rajkumar

Ghalib³

et al. 2022

Mobile Information Systems

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show abstract

“…Priya et al proposed a new method for detecting shot boundaries in video sequences by extracting edge strength using orthogonal vectors from blocks of the frames, and transition (boundary) identification procedure which was capable of detecting the shot or non shot boundaries in the video sequences [10]. The adaptive measuring method based on Traditional Color Histogram (TCH) and Histogram of Gradient Directions (HGD) was proposed by Lin et al for the threshold of distance as the basis of identifying shot changes between frames [11]. In paper [12], the propose algorithm extracted structure features from each video frame by using dual-tree complex wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

A Method of Video Shot-Boundary Detection based on Grey Modeling for Histogram Sequence

Liu¹,

Dai²

2016

IJSIP

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Video Shot-Boundary Detection is important for video analysis, segmentation and retrieval. The detection results of Shot-Boundary are not only relation with two nearest frames of shot cuts, but also their several neighboring frames. Video stream is multistream time sequence. Grey model based on set sequence (SGM) is proposed to analyze and process multi-stream time sequences constructed with sampled histograms in this paper. Consequently, a novel Shot-Boundary Detection method is put forward based on SGM and color histogram, which includes the following steps: (1) When a new video frame coming into the detection system, the image is transformed into intensity image under HSI color model; (2) Histogram of the video frame can be obtained. Then, taking samples for the histogram and constructing set sequence with proximate sampled histograms is implemented;(3) SGM is introduced to simulate the sampled histogram sequence in this detection method. Moreover, absolute mean error (AME) and regulative AME (R AME ) with thresholds are applied to make a detection judgment. Finally, the experiments illuminate that the proposed method is an effective Shot-Boundary Detection method, and it is superior to method of Histogram disparity significantly, especially when it is used to detect cuts with some complex video scenes.

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Multiple Grey-scale Feature Based Shot Boundary Detection

Jose

Rajkumar

2021

2021 Asian Conference on Innovation in Technology (ASIANCON)

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Analysis of Shot Boundary Based on Color and Texture Features of Frame

Cited by 3 publications

References 13 publications

Efficient Shot Boundary Detection with Multiple Visual Representations

Efficient Shot Boundary Detection with Multiple Visual Representations

A Method of Video Shot-Boundary Detection based on Grey Modeling for Histogram Sequence

Multiple Grey-scale Feature Based Shot Boundary Detection

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