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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 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...
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...
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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