Comparison of FCM and FISODATA

Fergani, B.; Kholladi, Mohamed-Khireddine; Bahri, Maroua

doi:10.5120/8913-2960

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…The proposed algorithm is tested with a simulated SAR image and four real SAR images. In addition, the comparing algorithms, including ISODATA [ 5 ], improved AGNES [ 8 ], HMRF-FCM [ 6 ] and Gamma-MRF [ 21 ] algorithms are also used to evaluate the proposed algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Many approaches have been studied to determine the number of clusters, such as clustering methods [ 5 , 6 , 7 , 8 , 9 ] and statistic methods [ 10 , 11 , 12 , 13 , 14 ]. The most popular clustering method is Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) [ 5 ], in which the number of clusters is changed by alternately splitting and merging clusters. However, it requires a lot of parameters given by humans, and the K-means dominating optimization in ISODATA algorithm is not suitable for segmenting SAR images because of the statistical characteristics of the speckle noises.…”

Section: Introductionmentioning

confidence: 99%

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Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering

Zhao

2017

Sensors

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This paper presents a novel multilook SAR image segmentation algorithm with an unknown number of clusters. Firstly, the marginal probability distribution for a given SAR image is defined by a Gamma mixture model (GaMM), in which the number of components corresponds to the number of homogeneous regions needed to segment and the spatial relationship among neighboring pixels is characterized by a Markov Random Field (MRF) defined by the weighting coefficients of components in GaMM. During the algorithm iteration procedure, the number of clusters is gradually reduced by merging two components until they are equal to one. For each fixed number of clusters, the parameters of GaMM are estimated and the optimal segmentation result corresponding to the number is obtained by maximizing the marginal probability. Finally, the number of clusters with minimum global energy defined as the negative logarithm of marginal probability is indicated as the expected number of clusters with the homogeneous regions needed to be segmented, and the corresponding segmentation result is considered as the final optimal one. The experimental results from the proposed and comparing algorithms for simulated and real multilook SAR images show that the proposed algorithm can find the real number of clusters and obtain more accurate segmentation results simultaneously.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering

Zhao

2017

Sensors

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“…Fuzzy Iterative Self-organizing Data Analysis Techniques Algorithm (F-ISODATA) is an extension of Fuzzy C-Means (FCM) clustering algorithm in fuzzy clustering [29], which updates the number of clusters during the clustering process. It first initializes the number of clusters, then merges the similar clusters and splits the dissimilar clusters in iterative way.…”

Section: Fuzzy Isodata Clusteringmentioning

confidence: 99%

Unobtrusive Sensing Incremental Social Contexts Using Fuzzy Class Incremental Learning

Chen¹,

Chen²,

Gao³

et al. 2015

2015 IEEE International Conference on Data Mining

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By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior, the built classifier seldom deals with newly appeared contexts, which results in degrading the recognition performance greatly. To address this problem, we propose, an OSELM (online sequential extreme learning machine) based class incremental learning method for continuous and unobtrusive sensing new classes of social contexts from dynamic Bluetooth data alone. We integrate fuzzy clustering technique and OSELM to discover and recognize social contextual behaviors by real-world Bluetooth sensor data. Experimental results show that our method can automatically cope with incremental classes of social contexts that appear unpredictably in the real-world. Further, our proposed method have the effective recognition capability for both original known classes and newly appeared unknown classes, respectively.

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Recognizing extended surrounding contexts via class incremental learning

Chen

Wang

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 AC

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Comparison of FCM and FISODATA

Cited by 3 publications

References 8 publications

Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering

Multilook SAR Image Segmentation with an Unknown Number of Clusters Using a Gamma Mixture Model and Hierarchical Clustering

Unobtrusive Sensing Incremental Social Contexts Using Fuzzy Class Incremental Learning

Recognizing extended surrounding contexts via class incremental learning

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