• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.80-85
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• 2011

Speaker change detection involves the identification of time indices of an audio stream, where the identity of the speaker changes. In this paper, we propose novel measures for the speaker change detection based on a graph-partitioning criterion over the pairwise distance matrix of feature-vector stream. Experiments on both synthetic and real-world data were performed and showed that the proposed approach yield promising results compared with the conventional statistical measures.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.61-70
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• 2023

Image segmentation is a very crucial step in effective digital image processing. In the past decade, several research contributions were given related to this field. However, a general segmentation algorithm suitable for various applications is still challenging. Among several image segmentation approaches, graph-based approach has gained popularity due to its basic ability which reflects global image properties. This paper proposes a methodology to partition the image with its pixel, region and texture along with its intensity. To make segmentation faster in large images, it is processed in parallel among several CPUs. A way to achieve this is to split images into tiles that are independently processed. However, regions overlapping the tile border are split or lost when the minimum size requirements of the segmentation algorithm are not met. Here the contributions are made to segment the image on the basis of its pixel using min-cut/max-flow algorithm along with edge-based segmentation of the image. To segment on the basis of the region using a homogenous optimum cut algorithm with boundary segmentation. On the basis of texture, the object type using spectral partitioning technique is identified which also minimizes the graph cut value.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2243-2245
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• 1998

In this paper, we treat image segmentation as a graph partitioning problem. and use the normalized cut for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different graphs as well as the total similarity within the groups. The minimization of this criterion can formulated as a generalized eigenvalues problem. We have applied this approach to segment static image. This criterion can be shown to be computed efficiently by a generalized eigenvalues problem

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.141-149
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• 2011

We consider the problem of assigning tasks to homogeneous nodes in the distributed system, so as to minimize the amount of communication, while balancing the processors' loads. This issue can be posed as the graph partitioning problem. Given an undirected graph G=(nodes, edges), where nodes represent task modules and edges represent communication, the goal is to divide n, the number of processors, as to balance the processors' loads, while minimizing the capacity of edges cut. Since these two optimization criteria conflict each other, one has to make a compromise between them according to the given task type. We propose a new cost function to evaluate static task assignments and a heuristic algorithm to solve the transformed problem, explicitly describing the tradeoff between the two goals. Simulation results show that our approach outperforms an existing representative approach for a variety of task and processing systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.734-736
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• 2019

그래프 분할 기법은 분산 환경에서 그래프 질의 수행에 있어 통신 비용을 줄이고 부하 균형을 맞추고자 그래프의 정점과 간선들을 여러 머신들에 나누어 저장하는 방법이다. 본 논문에서는 그래프 질의 수행에 관한 지식을 정리하고, 간선 절단 기법(edge-cut), 정점 절단 기법(vertex-cut), 하이브리드 절단 기법(hybrid-cut)으로 알려진 대표적인 그래프 분할 기법과 최신 그래프 시스템들의 그래프 분할 기법을 소개하고 비교한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.141-148
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• 2011

Most of the previous works on scene change detection algorithm focus on the detection of abrupt rather than gradual changes. In general, gradual scene change detection algorithms require heavy computation. Some of those approaches don't consider the error factors such as flashlights, camera or object movements, and special effects. Many scenes change detection algorithms based on the histogram show better performances than other approaches, but they have computation load problem. In this paper, we proposed a scene change detection algorithm with fast and accurate performance using the vertical and horizontal blocked slice images and their macro block informations. We apply graph cut partitioning algorithm for clustering and partitioning of video sequence using generated spatio-temporal histogram. When making spatio-temporal histogram, we only use the central block on vertical and horizontal direction for performance improvement. To detect camera and object movement as well as various special effects accurately, we utilize the motion vector and type information of the macro block.