• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.37-43
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• 2016

This paper introduces a method for video shot group detection needed for efficient management and summary of video. The proposed method detects shots based on low-level visual properties and performs temporal and spatial clustering based on visual similarity of neighboring shots. Shot groups created from temporal clustering are further clustered into small groups with respect to visual similarity. A set of representative shot frames are selected from each cluster of the smaller groups representing a scene. Shots excluded from temporal clustering are also clustered into groups from which representative shot frames are selected. A number of video clips are collected and applied to the method for accuracy of shot group detection. We achieved 91% of accuracy of the method for shot group detection. The number of representative shot frames is reduced to 1/3 of the total shot frames. The experiment also shows the inverse relationship between accuracy and compression rate.

• Journal of KIISE:Databases
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• v.29 no.3
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• pp.168-179
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• 2002

In this paper, we investigate video representation techniques that are the foundational work for the subsequent video processing such as video storage and retrieval. A video data set if a collection of video clips, each of which is a sequence of video frames and is represented by a multidimensional data sequence (MDS). An MDS is partitioned into video segments considering temporal relationship among frames, and then similar segments of the clip are grouped into video clusters. Thus, the video clip is represented by a small number of video clusters. The video segmentation and clustering algorithm, VDCluster, proposed in this paper guarantee clustering quality to south an extent that satisfies predefined conditions. The experiments show that our algorithm performs very effectively with respect to various video data sets.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.111-119
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• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with $\chi2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.144-147
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• 2010

Recently the change of content consumption trend activated the social video sharing platform and the video clip itself. There have been intensive interests and efforts to automatically abstract compact and meaningful video clips. In this paper, we propose a method which use the clustering of the bookmark data created by collective intelligence instead of using the video content analysis. The partitional clustering of points in 2-dimensional space derived from the bookmark data make it possible to abstract highlights effectively. The method is enhanced by the 1-dimensional accumulated bookmark count graph. Experiments on the real data from KBS internet service show the effectiveness of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.75-82
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• 2002

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper Propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with χ2 histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3239-3267
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• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.231-234
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• 2013

This paper introduces a mixture of background subtraction technique and K-Means clustering algorithm for removing shadows from video sequences. Lighting conditions cause an issue with segmentation. The proposed method can successfully eradicate artifacts associated with lighting changes such as highlight and reflection, and cast shadows of moving object from segmentation. In this paper, K-Means clustering algorithm is applied to the foreground, which is initially fragmented by background subtraction technique. The estimated shadow region is then superimposed on the background to eliminate the effects that cause redundancy in object detection. Simulation results depict that the proposed approach is capable of removing shadows and reflections from moving objects with an accuracy of more than 95% in every cases considered.

• KSCI Review
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• v.14 no.1
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• pp.255-264
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• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with ${\chi}^2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.159-162
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• 2000

In this paper, we propose an efficient method to detect shot changes in compressed MPEG video data by using reference features among video frames. The reference features among video frames imply the similarities among adjacent frames by prediction coded type of each frame. A shot change is detected if the similarity degrees of a frame and its adjacent frames are low. And the shot change detection algorithm is improved by using Fuzzy c-means (FCM) clustering algorithm. The FCM clustering algorithm uses the shot change probabilities evaluated in the mask matching of reference ratios and difference measure values based on frame reference ratios.