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Vehicle identification and traffic accident detection plays an important role in the Intelligent Transportation System. Vehicle image segmentation is the key technological foundation for further identification and detection processing. This article improves the inadequacy of Fuzzy C-Means (FCM) clustering algorithm by proposing the Spatial Constrained FCM (SCFCM) algorithm. Firstly, the each pixel's...
High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type...
In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant...
Real data often are comprised of multiple modalities or different views, which provide complementary and consensus information to each other. Exploring those information is important for the multi-view data clustering and classification. Multiview embedding is an effective method for multiple view data which uncovers the common latent structure shared by different views. Previous studies assumed that...
Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the...
A robust hyper spectral unmixing algorithm that finds multiple sets of end members is introduced. The algorithm, called Robust Context Dependent Spectral Unmixing (RCDSU), combines the advantages of context dependent unmixing and robust clustering. RCDSU adapts the unmixing to different regions, or contexts, of the spectral space. It combines fuzzy and possibilistic clustering and linear unmixing...
Fuzzy clustering algorithms have been widely used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm with robust spatially constraint for accurate and robust brain MR image segmentation. A novel spatial factor is proposed...
In this paper, we present a new Markov Random Field based FCM image segmentation algorithm. A new energy function is proposed to utilize the spatial and contextual information simultaneously. In the proposed energy function, we use a weighted distance to reflect the different effects of neighborhood pixels. By using the new energy function, the new algorithm has a better performance in noise-corrupted...
In this paper, we propose a method of image segmentation, which is based on superpixel and optimized spatial feature. In this paper, the superpixels are taken into account, which can reduce computational burden, and the result of over-segmentation can also be benefit for segmentation results. The main idea of this paper is based on the conventional fuzzy c-means (FCM). The conventional FCM has a better...
The clustering ensembles mingle numerous partitions of a specified data into a single clustering solution. Clustering ensemble has emerged as a potent approach for ameliorating both the forcefulness and the stability of unsupervised classification results. One of the major problems in clustering ensembles is to find the best consensus function. Finding final partition from different clustering results...
Nowadays, Smartphones have been widely used due to their capabilities in communication and multimedia processing. Smartphones provide access to a tremendous amount of sensitive information related to business, such as customer contacts, financial data, and Intranet networks. Hence, the Internet of the future will be mobile Internet. However, threat of malicious software has become an important factor...
Dynamic networks are commonly found in the current web age. In scenarios like social networks and social media, dynamic networks are noisy, are of large-scale and evolve quickly. In this paper, we focus on the cluster evolution tracking problem on highly dynamic networks, with clear application to event evolution tracking. There are several previous works on data stream clustering using a node-by-node...
Subspace clustering via Low-Rank Representation (LRR) has shown its effectiveness in clustering the data points sampled from a union of multiple subspaces. In original LRR, the noise in data is assumed to be Gaussian or sparse, which may be inappropriate in real-world scenarios, especially when the data is densely corrupted. In this paper, we aim to improve the robustness of LRR in the presence of...
Kernel clustering methods have been used successfully to cluster non linearly separable data. In this paper, we propose a modification of the Kernel K-means, called the Multi-Scale Kernel K-means, that addresses one important challenge, which is the automated estimation of the kernel scale parameters for data containing clusters with different scale values. We propose a novel method that estimates...
Principal curves, as a nonlinear generalization of principal components, are a common tool used in multivariate analysis for ends like dimensionality reduction and feature extraction. However, one of the difficulties that arise when utilizing this technique is that efficiency of existing principal curves algorithms is often low when dealing with large data set owing to high computational complexity...
This paper proposes a modified fuzzy c-regression models clustering algorithm based on Euclidean particle swarm optimization. The Fuzzy C-Regression Models (FCRM) clustering algorithm has a considerable sensitive to initialization susceptible to converge to a local minimum of the objective function. In order to overcome this problem, Euclidean particle swarm optimization is employed to optimize the...
Fuzzy clustering is a popular method for image segmentation and various of models based on fuzzy clustering are proposed. However, many methods suffer from the slow convergence and sensitivity to noise and parameters. In this letter, a novel fuzzy clustering method for image segmentation is proposed to solve these problems. A kernel which incorporates the local spatial information is proposed to regularize...
Kernel methods and Nonnegative matrix factorization (NMF) are both widely used in data mining and machine learning. The previous one is best known for its capability of transforming data into high dimension feature space, while the latter one is well known for its natural interpretations and good performance. In this paper, we propose a robust kernel NMF approach using L2, 1 norm loss function. Compared...
We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost....
Mode-seeking has been widely used as a powerful data analysis technique for clustering and filtering in a metric feature space. We introduce a versatile and efficient modeseeking method for “graph” representation where general embedding of relational data is possible beyond metric spaces. Exploiting the global structure of the graph by random walks, our method intrinsically combines modeseeking with...
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