• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3074-3091
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• 2019

With the widespread use of the Internet, services for providing large-capacity multimedia data such as video-on-demand (VOD) services and video uploading sites have greatly increased. VOD service providers want to be able to provide users with high-quality keyframes of high quality videos within a few minutes after the broadcast ends. However, existing keyframe extraction tends to select keyframes whose quality as a keyframe is insufficiently considered, and it takes a long computation time because it does not consider an HD class image. In this paper, we propose a keyframe selection method that flexibly applies multiple keyframe quality metrics and improves the computation time. The main procedure is as follows. After shot boundary detection is performed, the first frames are extracted as initial keyframes. The user sets evaluation metrics and priorities by considering the genre and attributes of the video. According to the evaluation metrics and the priority, the low-quality keyframe is selected as a replacement target. The replacement target keyframe is replaced with a high-quality frame in the shot. The proposed method was subjectively evaluated by 23 votes. Approximately 45% of the replaced keyframes were improved and about 18% of the replaced keyframes were adversely affected. Also, it took about 10 minutes to complete the summary of one hour video, which resulted in a reduction of more than 44.5% of the execution time.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.85-88
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• 2022

영상을 구성하는 프레임 중에 키프레임은 일반적으로 영상 정보를 효과적으로 요약하거나 용이한 분석을 위해 선정된다. 화상이 가진 의미는 인물/사물 등의 객체탐지를 통해 추출되는데, 기존의 키프레임 관련 연구는 영상이 가지는 의미를 반영하는 키프레임을 찾아내기 어렵다. 본 논문에서는 영상이 가지는 특정 의미가 있다고 할 때 이를 반영하는 키프레임을 효과적으로 추출하는 방법을 실험적으로 탐구하였다. 구체적으로 영상을 통할하는 의미를 피로라고 가정하고 영상의 졸음 인식 관련 연구에 사용되는 DDD 데이터셋을 이용하여 효과적인 키프레임 추출 기법을 적용해 보았으며, 실험 결과 졸음이라는 특정 정보에 대한 해석을 도울 수 있는 의미 있는 요약을 제공하는 키프레임들을 효과적으로 추출하는 분석 기법을 찾아낼 수 있었다.

• Journal of Broadcast Engineering
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• v.14 no.6
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• pp.661-675
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• 2009

The carelessly and illegally copied contents are raising serious social problem as internet and multimedia technologies are advancing. Therefore, development of video copy detection system must be settled without delay. In this paper, we propose the hierarchical video copy detection method that estimates similarity using statistical characteristics between original video and manipulated(transformed) copy video. We rank according to luminance value of video to be robust to spacial transformation, and choose similar videos categorized as candidate segments in huge amount of database to reduce processing time and complexity. The copy videos generally insert black area in the edge of the image, so we remove rig black area and decide copy or not by using statistical characteristics of original video and copied video with center part of frame that contains important information of video. Experiment results show that the proposed method has similar keyframe accuracy to reference method, but we use less memory to save feature information than reference's, because the number of keyframes is less 61% than that of reference's. Also, the proposed method detects if the video is copied or not efficiently despite expansive spatial transformations such as blurring, contrast change, zoom in, zoom out, aspect ratio change, and caption insertion.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.662-676
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• 2008

This paper presents a video copy detection algorithm that blocks online transfer of illegally copied DTV broadcast programs. Particularly, the proposed algorithm establishes a set of keyframes by detecting abrupt changes of luminance, and then exploits the spatio-temporal features of keyframes. Comparing with the preregistered features stored in the database of DTV broadcast programs, the proposed scheme performs a function of video filtering in order to distinguish whether an uploaded video is illegally copied or not. Note that we analyze only a set of keyframes instead of an entire video frame. Thus, it is highly efficient to identify illegal copied video when we deal with a vast size of broadcast programs. Also, we confirm that the proposed technique is robust to a variety of video edit-effects that are often applied by online video redistribution, such as apsect-ratio change, logo insertion, caption insertion, visual quality degradation, and resolution change (downscaling). In addition, we perform a benchmark test in which the proposed scheme outperforms previous techniques.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.83-98
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• 2015

Many TV viewers use mainly portal sites in order to retrieve information related to broadcast while watching TV. However retrieving information that people wanted needs a lot of time to retrieve the information because current internet presents too much information which is not required. Consequentially, this process can't satisfy users who want to consume information immediately. Interactive video is being actively investigated to solve this problem. An interactive video provides clickable objects, areas or hotspots to interact with users. When users click object on the interactive video, they can see additional information, related to video, instantly. The following shows the three basic procedures to make an interactive video using interactive video authoring tool: (1) Create an augmented object; (2) Set an object's area and time to be displayed on the video; (3) Set an interactive action which is related to pages or hyperlink; However users who use existing authoring tools such as Popcorn Maker and Zentrick spend a lot of time in step (2). If users use wireWAX then they can save sufficient time to set object's location and time to be displayed because wireWAX uses vision based annotation method. But they need to wait for time to detect and track object. Therefore, it is required to reduce the process time in step (2) using benefits of manual annotation method and vision-based annotation method effectively. This paper proposes a novel annotation method allows annotator to easily annotate based on face area. For proposing new annotation method, this paper presents two steps: pre-processing step and annotation step. The pre-processing is necessary because system detects shots for users who want to find contents of video easily. Pre-processing step is as follow: 1) Extract shots using color histogram based shot boundary detection method from frames of video; 2) Make shot clusters using similarities of shots and aligns as shot sequences; and 3) Detect and track faces from all shots of shot sequence metadata and save into the shot sequence metadata with each shot. After pre-processing, user can annotates object as follow: 1) Annotator selects a shot sequence, and then selects keyframe of shot in the shot sequence; 2) Annotator annotates objects on the relative position of the actor's face on the selected keyframe. Then same objects will be annotated automatically until the end of shot sequence which has detected face area; and 3) User assigns additional information to the annotated object. In addition, this paper designs the feedback model in order to compensate the defects which are wrong aligned shots, wrong detected faces problem and inaccurate location problem might occur after object annotation. Furthermore, users can use interpolation method to interpolate position of objects which is deleted by feedback. After feedback user can save annotated object data to the interactive object metadata. Finally, this paper shows interactive video authoring system implemented for verifying performance of proposed annotation method which uses presented models. In the experiment presents analysis of object annotation time, and user evaluation. First, result of object annotation average time shows our proposed tool is 2 times faster than existing authoring tools for object annotation. Sometimes, annotation time of proposed tool took longer than existing authoring tools, because wrong shots are detected in the pre-processing. The usefulness and convenience of the system were measured through the user evaluation which was aimed at users who have experienced in interactive video authoring system. Recruited 19 experts evaluates of 11 questions which is out of CSUQ(Computer System Usability Questionnaire). CSUQ is designed by IBM for evaluating system. Through the user evaluation, showed that proposed tool is useful for authoring interactive video than about 10% of the other interactive video authoring systems.