This study proposes an emotion clustering method based on Probabilistic Linear Discriminant Analysis (PLDA). Each emotional utterance is modeled as a GMM mean supervector. Hierarchical clustering is applied to cluster supervectors that represent similar emotions using a likelihood ratio from a PLDA model. The PLDA model can be trained with a different emotional database from the test data, with...

The appearance of multiple faces is influenced by abnormal exposure, interfering backgrounds or fake objects greatly in the color face image. A multiple-face detection method based on the adaptive dual skin model and improved fuzzy C-mean clustering was presented in this study. First an adaptive skin-color model and an adaptive skin-probability model were built to acquire the skin likelihood fo...

OBJECTIVE The aim of this study was to devise an objective clustering method for magnetoencephalography (MEG) interictal spike sources, and to identify the prognostic value of the new clustering method in adult epilepsy patients with cortical dysplasia (CD). METHODS We retrospectively analyzed 25 adult patients with histologically proven CD, who underwent MEG examination and surgical resectio...

The accurate and automatic segmentation of tissue regions in cervigram images can aid in the identification and classification of precancerous regions. We implement and analyze four GPU (Graphics Processing Unit) based clustering algorithms: K-means, mean shift, deterministic annealing, and spatially coherent deterministic annealing. From our results, we propose a novel parallel algorithm using...

The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and click streams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. D-Stream algorithm is an extended grid-based clustering algorithm for different dimen...

Clustering is associate automatic learning technique geared toward grouping a collection of objects into subsets or clusters. The goal is to form clusters that are coherent internally, however well completely different from one another. In plain words, objects within the same cluster ought to be as similar as potential, whereas objects in one cluster ought to be as dissimilar as potential from ...

Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on a surface constructed with a “shadow” kernel. For Gaussian kernels, mean shift is a gradient...

Planar structures are essential components of the urban landscape and automated extraction planar structure from LiDAR data is a fundamental step in solving complex mapping tasks such as building recognition and urban modelling. This paper presents a new and effective method for planar structure extraction from airborne LiDAR data based on spectral clustering of straight line segments. The stra...

Clustering streaming time series is a difficult problem. Most traditional algorithms are too inefficient for large amounts of data and outliers in them. In this paper, we propose a new clustering method, which clusters Biclipped (CBC) stream data. It contains three phrases, namely, dimensionality reduction through piecewise aggregate approximation (PAA), Bi-clipped process that clipped the real...