• International Journal of Contents
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• v.4 no.2
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• pp.24-28
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• 2008

In this paper, we propose a new approach for content-based video retrieval using non-parametric based motion classification in the shot-based video indexing structure. Our system proposed in this paper has supported the real-time video retrieval using spatio-temporal feature comparison by measuring the similarity between visual features and between motion features, respectively, after extracting representative frame and non-parametric motion information from shot-based video clips segmented by scene change detection method. The extraction of non-parametric based motion features, after the normalized motion vectors are created from an MPEG-compressed stream, is effectively fulfilled by discretizing each normalized motion vector into various angle bins, and by considering the mean, variance, and direction of motion vectors in these bins. To obtain visual feature in representative frame, we use the edge-based spatial descriptor. Experimental results show that our approach is superior to conventional methods with regard to the performance for video indexing and retrieval.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.631-644
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• 2002

In this paper efficient algorithms for content-based video retrieval using motion information are proposed, including temporal scale-invariant retrieval and temporal scale-absolute retrieval. In temporal scale-invariant video retrieval, the distance transformation is performed on each trail image in database. Then, from a given que교 trail the pixel values along the query trail are added in each distance image to compute the average distance between the trails of query image and database image, since the intensity of each pixel in distance image represents the distance from that pixel to the nearest edge pixel. For temporal scale-absolute retrieval, a new coding scheme referred to as Motion Retrieval Code is proposed. This code is designed to represent object motions in the human visual sense so that the retrieval performance can be improved. The proposed coding scheme can also achieve a fast matching, since the similarity between two motion vectors can be computed by simple bit operations. The efficiencies of the proposed methods are shown by experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.913-924
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• 2000

This paper proposes an efficient descriptor for objects motion trajectory and a video retrieval algorithm based on objects motion trajectory. The algorithm describes parameters with coefficients of 2-order polynomial for objects motion trajectory after segmentation of the object from the scene. The algorithm also identifies types, intervals, and magnitude of global motion caused by camera motion and indexes them with 6-affine parameters. This paper implements content-based video retrieval using similarity-match between indexed parameters and queried ones for objects motion trajectory. The proposed algorithm will support not only faster retrieval for general videos but efficient operation for unmanned video surveillance system.

• Journal of Broadcast Engineering
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• v.4 no.2
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• pp.187-196
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• 1999

This paper presents an efficient and automatic color and motion feature extraction algorithm for content-based MPEG-l video retrieval. Based on the proposed method. a video retrieval system is implemented. For color feature. the proposed algorithm considers dynamic color iRformation in video data, and thereby can overcome the limits of the previous key-frame based method. For motion feature, we utilize the motion vector in MPEG-l video with color information. and extract the color-motion feature. The proposed algorithm can solve the weakness of the previous location based motion feature method. Finally. the proposed method is evaluated within the implemented video retrieval system.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.133-136
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• 2001

Numerous challenges have been made to retrieve video using the contents. Recently MPEG-7 had set up a set of visual descriptors for such purpose of searching and retrieving multimedia data. Among them, color and motion descriptors are employed to develop a content-based video retrieval system to search for videos that have similar characteristics in terms of color and motion features of the video sequence. In this paper, the performance of the proposed system is analyzed and evaluated. Experimental results indicate that the processing time required for a retrieval using MPEG-7 descriptors is relatively short at the expense of the retrieval accuracy.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.541-546
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• 2009

There are many videos about sports. There is a large need for content based video retrievals. In sports videos, the motions and camera works have much information about shots and plays. This paper proposes the baseball game process understanding using the similar motion retrieval on videos. We can retrieve the similar motion parts based on motions shown in videos using the space-time images describing the motions. Using a finite state model of plays, we can decide the precise point of pitches from the pattern of estimated typical motions. From only the motions, we can decide the precise point of pitches. This paper describes the method and the experimental results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.149-157
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• 2001

In the video retrieval system, visual clues of still images and motion information of video are employed as feature vectors. We generate the temporal textures to express the motion information whose properties are simple expression, easy to compute. We make those temporal textures of wavelet coefficients to express motion information, M components. Then, temporal texture feature vectors are extracted using spatial texture feature vectors, i.e. spatial gray-level dependence. Also, motion amount and motion centroid are computed from temporal textures. Motion trajectories provide the most important information for expressing the motion property. In our modeling system, we can extract the main motion trajectory from the temporal textures.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.2-9
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• 2000

This paper presents a statistical motion activity description method and video retrievals by using the intensity and directions of the extracted motion vectors from video sequence. Since the proposed method can represent temporal and spatial cognitive characteristics of an entire video, several images between key frames, and images in a certain interval, it can be effectively applied to digital video services such as video retrieval, surveilance, multimedia database, and broadcasting filterings. In the paper, the effectiveness of the proposed algorithm is shown with a lot of shots of MPEG-7 video dataset.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.891-898
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• 2004

An effective video indexing is required to manipulate large video databases. Most algorithms for video indexing have been commonly used histograms, edges, or motion features. In this paper, we propose an efficient algorithm using the luminance projection for video retrieval. To effectively index the video sequences and to reduce the computational complexity, we use the key frames extracted by the cumulative measure, and compare the set of key frames using the modified Hausdorff distance. Experimental results show that the proposed video indexing and video retrieval algorithm yields the higher accuracy and performance than the conventional algorithm.