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Edited video recordings, such as talk-shows and sitcoms, often include Audio-Visual clusters: frequent repetitions of closely related acoustic and visual content. For example during a political debate, every time that a given participant holds the conversational floor, her/his voice tends to co-occur with camera views (i.e. shots) showing her/his portrait. Differently from the previous Audio-Visual...
This paper introduces a non-temporal multiple silhouettes in Hidden Markov Model (HMM) for offering view independent human posture recognition. The multiple silhouettes are used to reduce the ambiguity problem of posture recognition. A simple feature extraction of the 2D shape contour based histogram is used for image encoding and K-Means algorithm is applied for clustering and code-wording of eight...
The fuzzy HMM algorithm is regarded as an application of the fuzzy expectation-maximization (EM) algorithm to the Baum-Welch algorithm in the HMM. The Texas Instruments p4 used speech and speaker recognition experiments and show better results for fuzzy HMMs compared with conventional HMMs. Equation and how estimation of discrete and continuous HMM parameters on based this two algorithm is explained...
In this paper, an HMM-embedded unsupervised learning approach is proposed to detect the music events by grouping the similar segments of the music signal. This approach can cluster the segments based on their similarity of the spectral as well as the temporal structures. This is not easily done for clustering with the traditional similarity measures. Together with a Bayesian information criterion,...
Model-based clustering is one of the most important ways for time series data mining. However, the process of clustering may encounter several problems. In this paper, a novel clustering algorithm of time-series which incorporates recursive hidden Markov model(HMM) training is proposed. Our contributions are as follows: 1) We recursively train models and use these model information in the process...
The recognition of events in video data is a subject of much current interest. In this paper, we address several issues related to this topic. The first one is overfitting when very large feature spaces are used and relatively small amounts of training data are available. The second is the use of a framework that can recognise events at different time scales, as standard hidden Markov model (HMM)...
We develop a trajectory pattern learning method that has two significant advantages over past work. First, we represent trajectories in the HMM parameter space, thus we overcome the normalization problems of existing methods. Second, we determine common trajectory paths by analyzing the optimal cluster number rather than using a predefined number of clusters. We compute affinity matrices and apply...
In this paper we address the issues in construction of discrete hidden Markov models (HMMs) in the feature space of Mercer kernels. The kernel space HMMs are suitable for complex pattern recognition tasks that involve varying length patterns as in speech recognition. The main issues addressed are related to clustering and vector quantization in the kernel feature space for large data sets consisting...
Describes a method for learning classes of facial motion patterns from a video of a human interacting with a computerized embodied agent. The method also learns correlations between the discovered motion classes and the current interaction context. Our work is motivated by two hypotheses. First, a computer user's facial displays are context-dependent, especially in the presence of an embodied agent...
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