Video shot boundary detection using block based cumulative approach

Rashmi, B. S.; Nagendraswamy, H. S.

doi:10.1007/s11042-020-09697-6

Cited by 26 publications

(17 citation statements)

References 49 publications

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“…A gradient edge detector designed and used to detect edges. In edge-based segmentation algorithms, Canny's algorithm produced the best hash compared to Sobel and Prewitt [18]. The proposed methodology achieved acceptable results and the bacteria region separated from the CSF region.…”

Section: Discussionmentioning

confidence: 95%

Images Processing for Segmentation Neisseria Bacteria Cells

Ibrahim¹,

Almaleeh²,

Ahmed³

et al. 2021

World J. Adv. Res. Rev.

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This paper introduces the segmentation of Neisseria bacterial meningitis images. Images segmentation is an operation of identifying the homogeneous location in a digital image. The basic idea behind segmentation called thresholding, which be classified as single thresholding and multiple thresholding. To perform images segmentation, transformations and morphological operations processes are used to segment the images, as well as image transformation an edge detecting, filling operation, design structure element, and arithmetic operations technique is used to implement images segmentation. The images segmentation represent significant step in extracting images features and diagnoses the disease by computer software applications.

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Section: Discussionmentioning

confidence: 95%

Images Processing for Segmentation Neisseria Bacteria Cells

Ibrahim¹,

Almaleeh²,

Ahmed³

et al. 2021

World J. Adv. Res. Rev.

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“…The use of boundary gradients partially solves the problem of false positives when the camera or objects move within the frame, allowing you to use frame boundary matching without relying on lighting. Such a technique was used in [9] and [10]. In [11], the authors used object boundaries within frames to construct a histogram of directional gradients.…”

Section: Related Workmentioning

confidence: 99%

“…In this approach frame is divided into blocks (overlapping or not) and metric is calculated for each block. Vector of such metrics can be concatenated, histograms (including cumulative ones) [10]) or use statistics (e.g., expectation and variance). Partitioning into blocks allows the algorithm to be less susceptible to changes in certain parts of the scene (e.g., rapid movement of objects or flashes).…”

Section: Related Workmentioning

confidence: 99%

“…This approach is rarely used, as it is more advantageous to select thresholds for each individual video based on its features. The adaptive threshold does not have this disadvantage and can not only change depending on video [10], but also depending on metrics values in some neighborhood of the frame [20]. Thus, the threshold is adjusted not for the whole video, but for a particular scene.…”

Section: Related Workmentioning

confidence: 99%

“…• Cumulative color histogram metric -It is based on [10]. First, the Sobel operator is applied to the frames to find the edges, the trapezoidal smoothing function is applied, and the cumulative histogram of frame blocks is calculated.…”

Section: Base Featuresmentioning

confidence: 99%

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Shot Boundary Detection Method Based on a New Extensive Dataset and Mixed Features

Gushchin¹,

Antsiferova²,

Vatolin³

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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Shot boundary detection in video is one of the key stages of video data processing. A new method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed. The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2] datasets, and the MSU CC datasets, based on videos used in the video codec comparison conducted at MSU, as well as videos from the IBM set, were also plotted. The total dataset for algorithm development and testing exceeded the known TRECVID datasets. Based on the test results, the proposed algorithm for scene change detection outperformed its counterparts with a final F-score of 0.9794.

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Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Mishra

2022

Concurrency and Computation

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Summary This paper presents a hybrid feature extraction and optimized deep learning model for the effective shot boundary detection (SBD). A cross‐guided bilateral filtering technique is used for pre‐processing. A hybrid fuzzy histogram with dual tree complex wavelet transform (FH‐DTCWT) is developed for the feature extraction to extract the visual features from the pre‐processed frames of the block. Candidate segment selection process is performed to identify the non‐boundary frames from the extracted features to improve the accuracy. Continuity matrix is created using the possible transition frames to verify that the frames are in sequential order without any gap. Classification of transition types are performed by the proposed optimized deep convolutional neural network. Optimized deep learning (DCNN‐RBESO) model is the combination of deep convolutional neural network (DCNN) and Rider bald eagle search optimization (RBESO) algorithm. To update the weight of DCNN model in learning, RBESO is utilized. MATLAB tool is used to develop the proposed SBD model. The experimental analysis is carried out for the proposed model using TREC Video Retrieval Evaluation (TRECVID) and VideoSeg datasets. The proposed model outperformed in the detection of shot boundaries in terms of precision, recall, and F1‐score.

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Video shot boundary detection using block based cumulative approach

Cited by 26 publications

References 49 publications

Images Processing for Segmentation Neisseria Bacteria Cells

Images Processing for Segmentation Neisseria Bacteria Cells

Shot Boundary Detection Method Based on a New Extensive Dataset and Mixed Features

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

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