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With the increasing stress in working and studying, mental health becomes a major problem in the current social research. Generally, researchers can analyze psychological health states by using social perception behavior. The speech signal is an important research direction in this domain. It objectively assesses the mental health of social groups through the extraction and fusion of speech features...
Recent works have shown that hierarchical models lead to significant improvement in human activity recognition, which can not only enhance descriptive capability, but also improve discriminative power. However, most existing methods exploit just one of the two advantages. In this paper, a new hierarchical spatio-temporal model (HSTM) is proposed to integrate feature learning into two-layer hierarchical...
To reduce the huge consumption of traditional sensing, a multi-centers estimation based sensing scheme is proposed in this paper. Firstly, all potential channels are clustered into highly related groups with some channels selected as detecting channels (DCs) using an unsupervised algorithm. In each group, the states of other channels (estimated channels, ECs) are estimated according to their correlations...
Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper,...
This paper develops dynamic prediction hidden Markov models for arousal time curve estimation in sports videos. The method determines the arousal time curve by selecting a state sequence that maximizes the joint probability density function between the states and the arousal time curve. We derive the parameters using the expected maximization algorithm. Experiments were performed on several types...
This paper develops a novel method to automatically categorize different animation genres in a video database made for children, this is the first such research done in animation genre categorization. The method is based on statistically modeling the temporal texture attributes of the video sequence. The features are extracted from gray-level co-occurrence matrices and a hidden Markov models (HMM)...
In this paper, we present a new independent component analysis mixture vector quantization (ICAMVQ) method to summarize the video content. In particular, independent component analysis (ICA) is applied first to explore the characteristics of video data and build a compact 2D feature space. A new ICAMVQ method is then developed to find the optimized quantization codebook in ICA subspace. The optimal...
In this paper, we propose a new vector quantization method to create video thumbnail. In particular, we employ video time density function (VTDF) to explore the temporal characteristics of video data first. A VTDF-based temporal quantization is then applied to segment the whole video in time domain. The optimal number of segments is obtained by a temporal mean square error (TMSE)-based criterion....
In this paper, we propose a new Gaussian mixture vector quantization (GMVQ)-based method to summarize the video content. In particular, in order to explore the semantic characteristics of video data, we present a new feature extraction method using independent component analysis (ICA) and color histogram difference to build a compact 3D feature space first. A new GMVQ method is then developed to find...
In this paper, we present a new temporal quantization-based method using repeated weighted boosting search (RWBS) to navigate the video content non-uniformly. In particular, we formulate the rapid video navigation problem as a generic sampling problem. We present a video temporal density function (VTDF) based on the inter-frame mutual information to describe the time density of video activities. A...
In this paper, a new hidden conditional random field (HCRF) model with independent component analysis (ICA) mixture feature functions is developed for sports event classification. Unlike Hidden Markov Model (HMM), HCRF is a discriminative model without conditional independence assumption of observations, which is more suitable for video content analysis. According to the non-Gaussian property of sports...
In this paper, a framework that combines feature extraction, model learning, and likelihood computation, is presented for video event detection. First, the independent component analysis (ICA) is applied to the raw feature space to extract the spatial features. Then, a framework based on ICA mixture hidden Markov models (ICAMHMM) is used to exploit the spatial and temporal characteristics of the training...
This paper describes a novel method for the analysis of sequential data that exhibits strong non-Gaussianities. In particular, we extend the classical continuous hidden Markov model (HMM) by modeling the observation densities as a mixture of non-Gaussian distributions. In order to obtain a parametric representation of the densities, we apply the independent component analysis (ICA) mixture model to...
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