Fast and robust key frame extraction method for gesture video based on high-level feature representation

Yang, Huizhong; Tian, Qinghua; Zhuang, Qiaoli; Li, Linye; Liang, Qinglong

doi:10.1007/s11760-020-01783-4

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…At present, researchers have presented various video hashing schemes for many applications, 13 – 15 such as video recognition, copy detection, retrieval, and anti-counterfeit detection. These video schemes can be classified into two categories: hashing schemes based on spatial computation and hashing schemes based on spatial–temporal computation.…”

Section: Related Workmentioning

confidence: 99%

Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection

Yu,

Tang,

Liang

et al. 2024

J. Electron. Imag.

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Robust video hashing is an effective method of copy detection. But most existing schemes do not make effective classifications; thus their copy detection performances are unsatisfactory. To address these problems, we propose a robust video hashing based on deep feature and the quaternion generic Fourier descriptor (QGFD) for copy detection. In the proposed hashing scheme, an entropy-weighted secondary frame is calculated by weighting all frames in a video group via color entropy. Because color entropy can reflect frame color information, the incorporation of color entropy into each frame guarantees the discrimination of the proposed hashing scheme and makes the obtained entropy-weighted secondary frame discriminative. Next, the entropy-weighted secondary frames are used to extract deep features and geometric invariant features. The deep features are extracted from the low-frequency discrete cosine transform (DCT) coefficients of feature maps learned by three Inception modules of GoogLeNet. As the low-frequency DCT coefficients indicate feature map content and are stable, the deep features provide good discrimination and robustness. The geometric invariant features are extracted by QGFD. Because QGFD are geometric invariant, the use of QGFD can improve the robustness of the proposed hashing scheme. Hash is ultimately determined by quantifying and combining deep features and geometric invariant features. Many experiments were performed on open datasets and indicate that the proposed hashing scheme is superior to the evaluated hashing schemes in the task of copy detection and classification.

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Section: Related Workmentioning

confidence: 99%

Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection

Yu,

Tang,

Liang

et al. 2024

J. Electron. Imag.

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show abstract

“…They then replaced the original video frame sequence with the keyframe sequence for feature extraction, trading precision for recognition efficiency. In 2021, Yang et al [39] proposed a multi-channel gradient magnitude-frequency histogram (HGMF-MC) feature descriptor. Using this feature as a basis, they conducted differential operations on adjacent frames to extract video keyframes.…”

Section: Key Frame Extractionmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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With the emergence of deep learning methodologies, vision-based gesture recognition technology has continuously advanced. This paper primarily delves into four main stages of vision-based gesture recognition: gesture segmentation, gesture tracking, feature extraction, and gesture classification. It sequentially introduces pertinent techniques from representative literature spanning from 2018 to 2023. Based on this analysis, the current status of vision-based gesture recognition technology is examined, paving the way for predicting its future trends and developments.

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“…A novel keyframe selection technique has been put out by Huang and Wang (2020) that uses a self‐attention model to choose crucial frames inside of shots. A keyframe extraction technique for gesture videos is used by Yang et al (2020) to precisely extract the gesture keyframe without compromising the semantic data. Sridevi and Kharde (2020) summarized videos through the use of highlight detection.…”

Section: Literature Surveymentioning

confidence: 99%

Auto encoder with mode‐based learning for keyframe extraction in video summarization

Shambharkar,

Goel

2023

Expert Systems

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The exponential increase in video consumption has created new difficulties for browsing and navigating through video more effectively and efficiently. Researchers are interested in video summarization because it offers a brief but instructive video version that helps users and systems save time and effort when looking for and comprehending relevant content. Key frame extraction is a method of video summarization that only chooses the most important frames from a given video. In this article, a novel supervised learning method ‘TC‐CLSTM Auto Encoder with Mode‐based Learning’ using temporal and spatial features is proposed for automatically choosing keyframes or important sub‐shots from videos. The method was able to achieve an average F‐score of 84.35 on TVSum dataset. Extensive tests on benchmark data sets show that the suggested methodology outperforms state‐of‐the‐art methods.

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Fast and robust key frame extraction method for gesture video based on high-level feature representation

Cited by 12 publications

References 22 publications

Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection

Robust video hashing based on deep feature and quaternion generic Fourier descriptor for copy detection

Review of vision-based gesture recognition technology

Auto encoder with mode‐based learning for keyframe extraction in video summarization

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