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Superpixels are an oversegmentation of an image and popularly used as a preprocessing in many computer vision applications. Many state-of-the-art superpixel segmentation algorithms rely either on minimizing special energy functions or on clustering pixels in the effective distance space. While in this paper, we introduce a novel algorithm to produce superpixels based on the edge map by utilizing a...
As spectral clustering has the advantage of recognizing non-convex distribution, it has been widely used in image segmentation and other areas. However, when the spectral clustering algorithm deals with large size images, in order to get the affinity matrix, it costs a lot of computation time, so its applications are limited. To solve this problem, this paper proposes a fast image segmentation algorithm...
Diversity among base classifiers is known to be a necessary condition for improving ensemble learning performance. In this paper, methods of selective ensemble learning including hill-climbing selection, ensemble forward sequential selection, ensemble backward sequential selection and clustering selection are studied. To measure the diversity among base classifiers in ensemble learning, the entropy...
Innovation ability of Industrial clusters is an important measure of regional innovation. Industrial clusters with strong innovation ability can promote the development of innovative enterprises, which are the key element of regional innovation. Therefore, it is important to establish models to evaluate innovation ability for industry clusters. In this paper, an improved BP neural network model was...
DBSCAN is a typical density-based clustering algorithm, but it is time-consuming to ascertain the parameter Eps and it does not perform well on multi-density datasets because of the global parameter Eps. In this paper, we use must-link constraints to ascertain the parameter Eps for each density distribution effectively and automatically, which will be used to deal with multi-density data sets for...
Cluster ensembles method is considered as a robust and accurate alternative to single clustering runs. It mainly consists of both generation of individual member and fusion methods. In this paper, we study the cluster ensembles where individual members are obtained based on k-means clustering algorithm and fusion method of hierarchical clustering is used. Three consensus functions, which are single...
There are many complicated data in real world, clustering analysis should be able to find the clusters of different shapes and densities. The existing typical clustering algorithms do not perform well on multi-density data. A semi-supervised clustering algorithm for multi-density dataset SCMD is proposed. The pairwise constraints: must-link and cannot-link that reflect the distribution of multi-density...
Aiming at diversity being a necessary condition of the ensemble learning, we study method for improving diversity of the neural networks ensemble based on K-means clustering technique. In this paper, we propose a selecting approach that is first to train many classifiers through training set with neural network algorithm, and to classify data on validation set using classifiers. And then we use the...
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