An Improved Keyframe Extraction Method Based on HSV Colour Space

Qu, Zhong; Lin, Li-Dan; Gao, Tengfei; Wang, Yongkun

doi:10.4304/jsw.8.7.1751-1758

Cited by 20 publications

(10 citation statements)

References 14 publications

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“…Among these frames, some are detected as key frames. Whether a frame is a key frame or not is determined by calculating an error using equation (10) and comparing it with the threshold. If the error is greater than the threshold, the given frame is marked as a key frame.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Video key frame detection based on the restricted Boltzmann machine

Knop¹,

Kapuściński²,

Mleczko³

2015

J. Appl. Math. Comput. Mech.

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Abstract. In this paper we present a new method for key frame detection. Our approach is based on a well-known algorithm of the Restricted Boltzmann Machine (RBM), which is a pivotal step in our method. The frames are compared to the RBM matcher, which allows one to search for key frame in the video sequence. The Restricted Boltzmann Machine is one of sophisticated types of neural networks, which can process the probability distribution, and is applied to filtering image recognition and modelling. The learning procedure is based on the matrix description of RBM, where the learning samples are grouped into packages, and represented as matrices. Our research confirms a potential usefulness for video key frame detection. The proposed method provides better results for professional and high-resolution videos. The simulations we conducted proved the effectiveness of our approach. The algorithm requires only one input parameter.

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Section: Resultsmentioning

confidence: 99%

“…Parameters a, b and c correspond to weighting factors when calculating final entropy and their values are set to 0.9, 0.3 and 0.1 respectively [10]. To detect a new key frame we compare values of entropy between current key frame and subsequent frames.…”

Section: Entropy Methodsmentioning

confidence: 99%

Video key frame detection based on the restricted Boltzmann machine

Knop¹,

Kapuściński²,

Mleczko³

2015

J. Appl. Math. Comput. Mech.

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show abstract

“…Keyframe selection is important to generate an interesting manga book. According to our research, there are many existing methods [4,11,18,24] for keyframe selection. Wang 𝑒𝑡 𝑎𝑙 .…”

Section: Keyframe Selectionmentioning

confidence: 99%

Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation

Yang

et al. 2021

Preprint

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In this paper, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approach first extracts informative keyframes by analyzing the subtitles, and stylizes keyframes into comic-style images. Then, we propose a novel automatic multi-page layout framework, which can allocate the images across multiple pages and synthesize visually interesting layouts based on the rich semantics of the images (e.g., importance and inter-image relation). Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audios. Our method is able to vary balloon shapes and word sizes in balloons in response to different emotions, leading to more enriched reading experience. Once the balloons are generated, they are placed adjacent to their corresponding speakers via speaker detection. Our results show that our method, without requiring any user inputs, can generate high-quality comic pages with visually rich layouts and balloons. Our user studies also demonstrate that users prefer our generated results over those by state-of-the-art comic generation systems.CCS Concepts: • Computing methodologies → Computer vision tasks; Computer vision.

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“…Zhong Qu et al [96] present a system for key frame extraction and video summarization using pixel and histogram based methods. Method like Histogram intersection, non-uniform portioning and weighting are used for video shot segmentation.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Detection of Content based Video Retrieval

Gornale¹,

Babaleshwar²,

Babaleshwar³

2019

IJIGSP

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Content Based Video Retrieval (CBVR) System has been investigated over past decade it's rooted in many applications like developments and technologies. The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need. Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application. This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video. The experiment is carried out for the detection of ROI using ACF detector. The detection rate of ROI is observed competitive and satisfactory.

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An Improved Keyframe Extraction Method Based on HSV Colour Space

Cited by 20 publications

References 14 publications

Video key frame detection based on the restricted Boltzmann machine

Video key frame detection based on the restricted Boltzmann machine

Automatic Comic Generation with Stylistic Multi-page Layouts and Emotion-driven Text Balloon Generation

Analysis and Detection of Content based Video Retrieval

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