• Title/Summary/Keyword: closest-neighbor clustering

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Unsupervised Image Classification using Region-growing Segmentation based on CN-chain

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.20 no.3
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    • pp.215-225
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    • 2004
  • A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

RAG-based Hierarchical Classification (RAG 기반 계층 분류 (2))

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.22 no.6
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    • pp.613-619
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    • 2006
  • This study proposed an unsupervised image classification through the dendrogram of agglomerative clustering as a higher stage of image segmentation in image processing. The proposed algorithm is a hierarchical clustering which includes searching a set of MCSNP (Mutual Closest Spectral Neighbor Pairs) based on the data structures of RAG(Regional Adjacency Graph) defined on spectral space and Min-Heap. It also employes a multi-window system in spectral space to define the spectral adjacency. RAG is updated for the change due to merging using RNV (Regional Neighbor Vector). The proposed algorithm provides a dendrogram which is a graphical representation of data. The hierarchical relationship in clustering can be easily interpreted in the dendrogram. In this study, the proposed algorithm has been extensively evaluated using simulated images and applied to very large QuickBird imagery acquired over an area of Korean Peninsula. The results have shown it potentiality for the application of remotely-sensed imagery.

Denoising Mapping Utilizing Constellation Symmetry in Denoise-and-Forward Two-Way Relay Channels

  • Zheng, Jianping;Bai, Baoming;Li, Ying
    • ETRI Journal
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    • v.34 no.4
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    • pp.617-620
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    • 2012
  • The denoising mapping with the closest-neighbor clustering (CNC) method in denoise-and-forward two-way relay channels is studied. Specifically, the symmetry of the constellations in source terminals A and B is utilized to reduce the complexity of the CNC method. The specific case considered first to illustrate how the constellation symmetry works in the CNC method is the quadrature phase-shift keying constellation in A and B and the single-antenna deployment in all terminals. This case study shows that an enormous complexity reduction can be achieved. Next, the result is extended to multiple-antenna scenarios and square quadrature amplitude modulations.

Nested-Hierarchical Classification (Nested-Hierarchical 분류분석)

  • Lee, Sang-Hoon
    • Proceedings of the KSRS Conference
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    • 2007.03a
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    • pp.130-133
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    • 2007
  • 본 연구는 원격 탐사의 영상 처리에서 영상 분할의 상위 수준으로 웅집 계층 clustering의 dendrogram을 통한 무감독 영상 분류를 제안한다. 제안된 알고리즘은 분광 영역에서 정의된 RAG(Regional Agency Graph)와 min-heap 자료 구조를 이용하여 MCSNP(Mutual Closest Spectral Neighbor Pair)의 집 합을 검색하면서 합병을 수행하는 계층 clustering 방법이다. 계산 시간과 저장 기억의 사용에 대한 효율을 증가시키기 위해 분광적 인접성올 정의 하는 분광 공간(spectral space)내의 다중창을 사용하였고 RNV(Region Neighbor Vector)을 이용하여 합병에 의하여 변하는 RAG 갱신하였고 적정한 단계 수가 주어 진다면 제안된 알고리즘은 집단 합병의 계층적 관계를 쉽게 해석 할 수 있는 dendrogram을 생성한다. 본 연구는 생성된 dendrogram을 이용한 nested-hierarchical 분석을 통하여 피복 형태의 계층적 관계를 해석한다. 이러한 해석은 피복 형태의 정확한 분류를 위한 의사 결정에 중요한 정보를 공급한다.

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Isolation and Characterization of a Bacteriophage Preying an Antifungal Bacterium

  • Rahimi-Midani, Aryan;Kim, Kyoung-Ho;Lee, Seon-Woo;Jung, Sang Bong;Choi, Tae-Jin
    • The Plant Pathology Journal
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    • v.32 no.6
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    • pp.584-588
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    • 2016
  • Several Bacillus species were isolated from rice field soils, and 16S rRNA gene sequence analysis showed that Bacillus cereus was the most abundant. A strain named BC1 showed antifungal activity against Rhizoctonia solani. Bacteriophages infecting strain BC1 were isolated from the same soil sample. The isolated phage PK16 had an icosahedral head of $100{\pm}5nm$ and tail of $200{\pm}5nm$, indicating that it belonged to the family Myoviridae. Analysis of the complete linear dsDNA genome revealed a 158,127-bp genome with G + C content of 39.9% comprising 235 open reading frames as well as 19 tRNA genes (including 1 pseudogene). Blastp analysis showed that the proteins encoded by the PK16 genome had the closest hits to proteins of seven different bacteriophages. A neighbor-joining phylogenetic tree based on the major capsid protein showed a robust clustering of phage PK16 with phage JBP901 and BCP8-2 isolated from Korean fermented food.