• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.137-142
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• 1997

One of the inherent problems in constructing the sign language dictionary is how to make retrieval and comparison operations on the visual database of signs. This paper describes a retrieval method, especifically for Japanese signs. This method has a useful capability for flexible retrieval of the sign from a bilingual dictionary. Our method can retrieve similar signs to the given input. The retrieval mechanism is essentially based on similarity between the given verbal description and verbal descriptions in a retrieval database. The similarity measure of verbal descriptions can be considered as the approximations for the similarity of sign motion images. As a results of our experiment, the success ratio of the retrievals is 96% in averages.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.393-398
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• 2003

Video which contains the multiple data such as text, images, audio and motion of objects is typical multimedia data. Multimedia retrieval system using XML is essential for efficient rep. of multimedia data. Therefore, multimedia retrieval system for retrieval and structural understanding is needed to retrieve the multimedia data. This Paper Proposes the multimedia retrieval system based on XML Meta-data modeling techniques.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.72-78
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• 2006

In this paper, we present a video shot retrieval algorithm in H.264/AVC compression domain. Unlike previous standards such as MPEG-2 and 4, H.264/AVC supports a variable block size for motion compensation. Therefore, existing video retrieval algorithms exploiting the motion vectors in MPEG-2 and 4 domains are not appropriate for H.264/AVC. So, we devise a method to project motion vectors with larger than $4{\times}4$ block sizes into those for the smallest $4{\times}4$ blocks. It also uses correlations among features for the measure of similarity. Experimental results with standard videos of 10558 frames and commercial videos of 48161 frames show that the proposed method yields ANMRR less than 0.2.

• Journal of the Korea Computer Graphics Society
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• v.19 no.2
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• pp.1-8
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• 2013

Captured human motion data has been widely utilized for understanding the mechanism of human motion and synthesizing the animation of virtual characters. Searching for desired motions from given motion data is an important prerequisite of analyzing and editing those selected motions. This paper presents a new method of content-based motion retrieval without the need of additional metadata such as keywords. While existing search methods have focused on skeletal configurations of body pose or planar trajectories of locomotion, our method receives a three-dimensional trajectory as its input query and retrieves a set of motion intervals in which the trajectories of body parts such as hands, foods, and pelvis are similar to the input trajectory. In order to allow the user to intuitively sketch spatial trajectories, we used the Leap Motion controller that can precisely trace finger movements as the input device for our experiments. We have evaluated the effectiveness of our approach by conducting a user study in which the users search for dozens of pre-selected motions from baseketball motion data including a variety of moves such as dribbling and shooting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.142-148
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• 2014

A descriptor which is suitable for motion analysis by using the motion features of moving objects from the real time image sequence is proposed. To segment moving objects from the background, the background learning is performed. We extract motion trajectories of individual objects by using the sequence of the 1st order moment of moving objects. The center points of each object are managed by linked list. The descriptor includes the 1st order coordinates of moving object belong to neighbor of the pre-defined position in grid pattern, The start frame number which a moving object appeared in the scene and the end frame number which it disappeared. A video retrieval by the proposed descriptor combining global and local feature is more effective than conventional methods which adopt a single feature among global and local features.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• Journal of Korea Multimedia Society
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• v.10 no.1
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• pp.117-127
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• 2007

As the value of the multimedia data is getting high, the study on the semantic recognition and retrieval about the multimedia information is strongly demanded. In this paper, we build the motion ontology and adopt it for representing the meaning of the moving objects in video data. By referencing the WordNet structure, we extend its semantic meaning based on the reclassification of motion verbs, which are used to represent the semantic meaning of moving objects. The represented information is receded in OWL/RDF(S). Here, we could expect the 'Is-A' and 'Equivalent' reasoning of the data as we use the ontologies. And the semantic representation about the moving objects is possible through the video annotation using ontology. And we tested the accuracy of the system comparing with the key-word based system. As a result, we could get the approximately 10% improvement of the system performance.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.1-11
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• 2009

Recently there has arisen concern in both the database community and the graphics society about data retrieval from large motion databases because the high dimensionality of motion data implies high costs. In this circumstance, finding an effective distance measure and an efficient query processing method for such data is a challenging problem. This paper presents an elaborate motion query processing system, SMoFinder (Similar Motion Finder), which incorporates a novel kinematic distance measure and an efficient indexing strategy via adaptive frame segmentation. To this end, we regard human motions as multi-linkage kinematics and propose the weighted Minkowski distance metric. For efficient indexing, we devise a new adaptive segmentation method that chooses representative frames among similar frames and stores chosen frames instead of all frames. For efficient search, we propose a new search method that processes k-nearest neighbors queries over only representative frames. Our experimental results show that the size of motion databases is reduced greatly (${\times}1/25$) but the search capability of SMoFinder is equal to or superior to that of other systems.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.107-115
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• 2011

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.