• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.235-237
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• 2022

Video content is divided into long-form video content and short-form video content according to the length. Long form video content is created with a length of 15 minutes or longer, and all frames of the captured video are included without editing. Short-form video content can be edited to a shorter length from 1 minute to 15 minutes, and only some frames from the frames of the captured video. Due to the recent growth of the single-person broadcasting market, the demand for short-form video content to increase viewers is increasing. Therefore, there is a need for research on content editing technology for editing and generating short-form video content. This study studies the technology to create short-form videos of main scenes by capturing images, voices, and motions. Short-form videos of key scenes use a pre-trained highlight extraction model through machine learning. An automatic video editing system and method for automatically generating a highlight video is a core technology of short-form video content. Machine learning-based automatic video editing method and system research will contribute to competitive content activities by reducing the effort and cost and time invested by single creators for video editing

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.131-148
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• 2008

The purposes of the study are to design a key-frame extraction algorithm for constructing the virtual storyboard surrogates and to evaluate the efficiency of the proposed algorithm. To do this, first, the theoretical framework was built by conducting two tasks. One is to investigate the previous studies on relevance and image recognition and classification. Second is to conduct an experiment in order to identify their frames recognition pattern of 20 participants. As a result, the key-frame extraction algorithm was constructed. Then the efficiency of proposed algorithm(hybrid method) was evaluated by conducting an experiment using 42 participants. In the experiment, the proposed algorithm was compared to the random method where key-frames were extracted simply at an interval of few seconds(or minutes) in terms of accuracy in summarizing or indexing a video. Finally, ways to utilize the proposed algorithm in digital libraries and Internet environment were suggested.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.51-56
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• 2019

This paper proposes an improved heartbeat signal extraction method for ballistocardiography(BCG)-based heart-rate measurement on mobile environment. First, from a mobile facial video, a handshake-free head motion signal is extracted by tracking facial features and background features at the same time. Then, a novel signal periodicity computation method is proposed to accurately separate out the heartbeat signal from the head motion signal. The proposed method could robustly extract heartbeat signals from mobile facial videos, and enabled more accurate heart rate measurement (measurement errors were reduced by 3-4 bpm) compared to the existing method.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.316-326
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• 2022

The prompt development in the field of video is the outbreak of online services which replaces the television media within a shorter period in gaining popularity. The online videos are encouraged more in use due to the captions displayed along with the scenes for better understandability. Not only entertainment media but other marketing companies and organizations are utilizing videos along with captions for their product promotions. The need for captions is enabled for its usage in many ways for hearing impaired and non-native people. Research is continued in an automatic display of the appropriate messages for the videos uploaded in shows, movies, educational videos, online classes, websites, etc. This paper focuses on two concerns namely the first part dealing with the machine learning method for preprocessing the videos into frames and resizing, the resized frames are classified into multiple actions after feature extraction. For the feature extraction statistical method, GLCM and Hu moments are used. The second part deals with the deep learning method where the CNN architecture is used to acquire the results. Finally both the results are compared to find the best accuracy where CNN proves to give top accuracy of 96.10% in classification.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.3
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• pp.165-178
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• 2007

The recent progress in multimedia signal processing and transmission technologies has contributed to the extensive use of multimedia devices to watch sports games with small LCD panel. However, the most of video sequences are captured for normal viewing on standard TV or HDTV, for cost reasons, merely resized and delivered without additional editing. This may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera techniques, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Moreover, it is also difficult to recognize the contents of the scorebox which contains the elapsed time and scores. This renuires intelligent display technique to provide small-display-viewers with better experience. To this end, one of the key technologies is to determine region of interest (ROI) and display the magnified ROI on the screen, where ROI is a part of the scene that viewers pay more attention to than other regions. Examples include a region surrounding a ball in long-shot and a scorebox located in the comer of each frame. In this paper, we propose a scheme for raising viewing experiences of multimedia mobile device users. Instead of taking generic approaches utilizing visually salient features for extraction of ROI in a scene, we take domain-specific approach to exploit unique attributes of the soccer video. The proposed scheme consists of two modules: ROI determination and scorebox extraction. The experimental results show that the proposed scheme offers useful tools for intelligent video display on multimedia mobile devices.

• Journal of Korea Game Society
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• v.12 no.3
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• pp.59-68
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• 2012

This paper describes the design and implementation of a network-adaptive N-screen game system to be used in cloud computing. The system we are considering needs to generate game video in a cloud server and transmit the video to multiple game devices over an in-home wireless network via a home game server. It is difficult to support multiple screens which have different resolutions with a single bitstream of game video. Therefore, we developed a new network-adaptive game-video extraction and transmission method using (1) scalable video coding and (2) Raptor code techniques. The simulation, conducted with real game-video, verified the efficiency of the proposed video streaming system.

• ETRI Journal
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• v.35 no.6
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.294-300
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• 2004

According to the development of digital media technologies various algorithms for video clip matching have been proposed to match the video sequences efficiently. A large number of video search methods have focused on frame-wise query, whereas a relatively few algorithms have been presented for video clip matching or video shot matching. In this paper, we propose an efficient algorithm to index the video sequences and to retrieve the sequences for video clip query. To improve the accuracy and performance of video sequence matching, we employ the Cauchy function as a similarity measure between histograms of consecutive frames, which yields a high performance compared with conventional measures. The key frames extracted from segmented video shots can be used not only for video shot clustering but also for video sequence matching or browsing, where the key frame is defined by the frame that is significantly different from the previous frames. Experimental results with color video sequences show that the proposed method yields the high matching performance and accuracy with a low computational load compared with conventional algorithms.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.51-61
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• 2017

This paper proposes a method to generate panoramic images by combining conventional feature extraction algorithms (e.g., SIFT, SURF, MPEG-7 CDVS) with sensed data from an inertia sensor to enhance the stitching results. The challenge of image stitching increases when the images are taken from two different mobile phones with no posture calibration. Using inertia sensor data obtained by the mobile phone, images with different yaw angles, pitch angles, roll angles are preprocessed and adjusted before performing stitching process. Performance of stitching (e.g., feature extraction time, inlier point numbers, stitching accuracy) between conventional feature extraction algorithms is reported along with the stitching performance with/without using the inertia sensor data.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.297-303
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• 2007

In this paper, the stroke-based text extraction algorithm for digital video is proposed. The proposed algorithm consists of four stages such as text detection, text localization, text segmentation and geometric verification. The text detection stage ascertains that a given frame in a video sequence contains text. This procedure is accomplished by morphological operations for the pixels with higher possibility of being stroke-based text, which is called as seed points. For the text localization stage, morphological operations for the edges including seed points ate adopted followed by horizontal and vortical projections. Text segmentation stage is to classify projected areas into text and background regions according to their intensity distribution. Finally, in the geometric verification stage, the segmented area are verified by using prior knowledge of video text characteristics.