Video Shot Boundary Detection and Key Frame Extraction for Video Retrieval

Kumar, G. S. Naveen; Reddy, V. S. K.; Kumar, S. Srinivas

doi:10.1007/978-981-10-8228-3_51

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…Kumar et al [13] used the SIFT for SBD and keyframe extraction with some frame elimination to reduce the time complexity. CIEDE2000 colour difference and mean luminance pattern are used in [14] for AT and GT detection, respectively.…”

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

“…Also, given that the video data used in this paper were recorded from the facades of a building with very low textures, among other challenges, we can mention feature extraction and matching algorithms in thermal infrared frames. In this regard, instead of using Kanade-Lucas-Tomasi (KLT) feature tracker algorithms in keyframes extraction methods, the proposed method utilizes the Scale-Invariant Feature Transform (SIFT) algorithm and matching key points (Suhr, 2009;Kumar, 2018;Wang, 2022). The steps in the keyframe extraction method presented in this paper are as follows: (1) the ability to recognize and remove blur frames from a sequence of thermal infrared video recorded frames.…”

Section: Introductionmentioning

confidence: 99%

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

Motayyeb

Samadzadegan

Javan

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Keyframes extraction is required and effective for the 3D reconstruction of objects from a thermal video sequence to increase geometric accuracy, reduce the volume of aerial triangulation calculations, and generate the dense point cloud. The primary goal and focus of this paper are to assess the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated. The method of keyframes extraction of thermal infrared video presented in this paper consists of three basic steps. (A) The ability to identify and remove blur frames from non-blur frames in a sequence of recorded frames. (B) The ability to apply the standard baseline condition between sequence frames to establish the overlap condition and prevent the creation of degeneracy conditions. (C) Evaluating degeneracy conditions and keyframes extraction using Geometric Robust Information Criteria (GRIC). The performance evaluation criteria for keyframes extraction in the generation of the thermal infrared dense point cloud in this paper are to assess the increase in density of the generated three-dimensional point cloud and reduce reprojection error. Based on the results and assessments presented in this paper, using keyframes increases the density of the thermal infrared dense point cloud by about 0.03% to 0.10% of points per square meter. It reduces the reprojection error by about 0.005% of pixels (2 times).

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“…In the field of video retrieval, the accuracy of shot segmentation will directly affect the performance of video retrieval systems. Therefore, how to improve the accuracy of shot segmentation is one of the difficult problems in video analysis [1]. In multimedia information, videos can be divided into four different levels of frames, shots, scenes, and video streams according to different granularities.…”

Section: Introductionmentioning

confidence: 99%

Visual Cognitive Mechanism Guided Video Shot Segmentation

Shao

2019

Cognitive Computing – ICCC 2019

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Shot segmentation of video sequences is one of the key technologies in video information processing, especially video retrieval. Traditional shot segmentation methods have low detection rate for the gradient shot and the abrupt shot, especially in a single scene. To deal with this problem, this paper proposes a video segmentation method based on visual cognition mechanism. This method proposes a block granularity color histogram to strengthen the visual salient area, and a highlight measure to describe the difference between the front and back frames. This brings great improvements to the accuracy of detecting shot switching in a single scene. In addition, based on the brightness visual perception in video, the difference between adjacent multi-frames in the sliding window is used to capture the brightness change for the gradient shots. Comparing with traditional methods, the proposed algorithm achieves better segmentation effect and has higher precision and recall rate.

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Video Shot Boundary Detection and Key Frame Extraction for Video Retrieval

Cited by 12 publications

References 13 publications

Efficient Shot Boundary Detection with Multiple Visual Representations

Efficient Shot Boundary Detection with Multiple Visual Representations

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

Visual Cognitive Mechanism Guided Video Shot Segmentation

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