Detection of hard cuts and gradual transitions from video using fuzzy logic

Das, Sudeshna; Sural, Shamik; Majumdar, A. K.

doi:10.1504/ijaisc.2008.021265

Cited by 13 publications

(14 citation statements)

References 32 publications

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“…Ellouze et al repaired the over-segmentation problem by classifying sections based on tempo features and fuzzy CMeans [3]. Das et al defined a unified interval type-2 fuzzy rule-based model using a fuzzy histogram and fuzzy co-occurrence matrix to detect cuts and various types of gradual transitions [4]. Based on a singular value decomposition (SVD), Bendraou et al proposed a video segmentation method, which employed the Frobenius norm of low rank approximation matrices, in order to perform an adaptive feature extraction [5].…”

Section: A the Rules-based Methodsmentioning

confidence: 99%

User-Guided Clustering for Video Segmentation on Coarse-Grained Feature Extraction

Peng

Wang

et al. 2019

IEEE Access

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Video segmentation is the task of temporally dividing a video into semantic sections, which are typically based on a specific concept or a theme that is usually defined by the user's intention. However, previous studies of video segmentation have that far not taken a user's intention into consideration. In this paper, a two-stage user-guided video segmentation framework has been presented, including dimension reduction and temporal clustering. During the dimension reduction stage, a coarse granularity feature extraction is conducted by a deep convolutional neural network pre-trained on ImageNet. In the temporal clustering stage, the information of the user's intention is utilized to segment videos on time domain with a proposed operator, which calculates the similarity distance between dimension reduced frames. To provide more insight into the videos, a hierarchical clustering method that allows users to segment videos at different granularities is proposed. Evaluation on Open Video Scene Detection(OVSD) dataset shows that the average F-score achieved by the proposed method is 0.72, even coarse-grained feature extraction is adopted. The evaluation also demonstrated that the proposed method can not only produce different segmentation results according to the user's intention, but it also produces hierarchical segmentation results from a low level to a higher abstraction level. INDEX TERMSClustering methods, dimension reduction, feature extraction, user centered design, video segmentation.

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Section: A the Rules-based Methodsmentioning

confidence: 99%

User-Guided Clustering for Video Segmentation on Coarse-Grained Feature Extraction

Peng

Wang

et al. 2019

IEEE Access

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“…Traditional grey modeling is proposed by Deng [27], and some principles and definitions are given here. The original sequence is x (0) : (1) be the AGO generation sequence of x (0) , x (1) =AGO x (0) can be expressed as x (1) = (x (1) (1), x (1) (2), , x (1) (n)), x (1)…”

Section: Traditional Grey Modelmentioning

confidence: 99%

“…Video Shot-Boundary Detection is important for video analysis, video segmentation and video retrieval. A video shot is a sequence of frames taken by one camera during a single continues action in time and space [1,2]. In real-time shot segmentation, the detection system must make a judgment automatically whether it is the beginning of another shot when a new video frame is coming.…”

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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“…In [17], histogram differences of consecutive frames are characterized as fuzzy terms, such as small, significant and large, and fuzzy rules for detecting abrupt and gradual transitions are formulated in a fuzzy-logic-based framework for segmentation of video sequences. Das et al [18] define a unified interval type-2 fuzzy rule based model using fuzzy histogram and fuzzy co-occurrence matrix to detect cuts and various types of gradual transitions.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy color histogram-based video segmentation

Küçüktunç

Güdükbay

Ulusoy

2010

Computer Vision and Image Understanding

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a b s t r a c tWe present a fuzzy color histogram-based shot-boundary detection algorithm specialized for contentbased copy detection applications. The proposed method aims to detect both cuts and gradual transitions (fade, dissolve) effectively in videos where heavy transformations (such as cam-cording, insertions of patterns, strong re-encoding) occur. Along with the color histogram generated with the fuzzy linking method on L*a*b* color space, the system extracts a mask for still regions and the window of picture-in-picture transformation for each detected shot, which will be useful in a content-based copy detection system. Experimental results show that our method effectively detects shot boundaries and reduces false alarms as compared to the state-of-the-art shot-boundary detection algorithms.

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Detection of hard cuts and gradual transitions from video using fuzzy logic

Cited by 13 publications

References 32 publications

User-Guided Clustering for Video Segmentation on Coarse-Grained Feature Extraction

User-Guided Clustering for Video Segmentation on Coarse-Grained Feature Extraction

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

Fuzzy color histogram-based video segmentation

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