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An approach for an efficient clustering of 3D line segments based on an unsupervised competitive neural network is applied to a set of high resolution satellite image data in this paper. The unsupervised competitive neural network, called centroid neural network for clustering 3D line segments (CNN-3D), utilizes the characteristics of 3D line segments. Successful application of CNN-3D can lead accurate...
A novel approach for an efficient extraction of rectangular boundaries from aerial image data is proposed in this paper. In this approach, a Centroid Neural Network (CNN) with a metric of line segments is utilized for connecting low-level linear structures or grouping similar objects. The proposed an approach, called hierarchical clustering method, utilizes the fact that rooftops of a building are...
Classification of image data by using Partitioned-Feature based Classifier (PFC)is proposed in this paper. The PFC does not use concatenated feature vectors extracted from the original data at once to classify each datum, but uses extracted feature vectors to classify data separately. In the training stage, the contribution rate calculated from each feature vector group is drawn throughout the accuracy...
A new model called intuitive fuzzy c-means (IFCM) model is proposed for the segmentation of magnetic resonance image in this paper. Fuzzy c-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in...
A classification method of video data using centroid neural network is proposed in this paper. The CNN algorithm is used for clustering the MPEG video data. In comparison with other conventional algorithms, The CNN requires neither a predetermined schedule for learning gain nor the total number of iterations for clustering. It always converges to sub-optimal solutions while conventional algorithms...
Fuzzy C-means (FCM) is one of the most widely used clustering algorithms and assigns memberships to which are inversely related to the relative distance to the point prototypes that are cluster centers in the FCM model. In order to overcome the problem of outliers in data, several models including possibilistic C-means (PCM) and possibilistic-fuzzy C-means (PFCM) models have been proposed. A new model...
A feature weighting procedure for centroid neural network (FWP-CNN) is proposed in this paper. The proposed FWP-CNN evaluates the importance of each feature in data by introducing a feature weighting concept to the CNN in the proposed algorithm. The use of feature weighting makes it possible to reject noises in data and thereby achieves a better clustering performance. Experimental results on a synthetic...
An unsupervised competitive neural network for efficient clustering of Gaussian probability density function (GPDF) data of continuous density hidden Markov models (CDHMMs) is proposed in this paper. The proposed unsupervised competitive neural network, called the divergence-based centroid neural network (DCNN), employs the divergence measure as its distance measure and utilizes the statistical characteristics...
Texture analysis has been efficiently utilized in the area of terrain classification. The widely used co-occurrence features have been reported most effective for this application. Since the number of co-occurrence features is very high, a terrain classifier based on co-occurrence features should deal with high dimensionality problem. This paper deals with how to solve high dimensionality problems...
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