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Lack of human prior knowledge is one of the main reasons that semantic gap still remains when it comes to automatic multimedia understanding. In this work, we exploit the ontological structure of target concepts and propose an universal ontological inference framework for image understanding. The framework explicitly utilizes subclass and co-occurrence relation to effectively refine the coarse concept...
Structured Support Vector Machine (SVM) is a recently developed extension of the very successful SVM approach, which can efficiently classify structured pattern with maximized margin. This paper presents an initial attempt for phoneme recognition using structured SVM. We simply learn the basic framework of HMMs in configuring the structured SVM. In the preliminary experiments with TIMIT corpus, the...
This study applies a novel neural-network technique, support vector regression (SVR), to predict reliably in dynamical system. The aim of this study is to examine the feasibility of SVR in state prediction by comparing it with the existing neural-network approaches. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which...
This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of features, and then a global Gaussian Mixture Model (GMM) learned from all images is used to randomly distribute each feature into one Gaussian component by a multinomial trial. The parameters of the multinomial distribution are...
The 3D facial geometry contains ample information about human facial expressions. Such information is invariant to pose and lighting conditions, which have imposed serious hurdles on many 2D facial analysis problems. In this paper, we perform person and gender independent facial expression recognition based on properties of the line segments connecting certain 3D facial feature points. The normalized...
The traditional support vector machine (SVM) often has an over-fitting problem when outliers exit in the training data set. Fuzzy support vector machine (FSVM) provides an effective approach to deal with the problem. It can reduce the effects of outliers by fuzzy membership functions. Choosing a proper fuzzy membership is very important. In this paper, a new fuzzy membership function is proposed to...
In this paper, we compare the state-of-the-art algorithms for text-independent speaker identification under adverse far-field recording conditions with extremely short training and testing utterances. The algorithms include both the generative and discriminative methods. For the generative methods, three variants of the original Gaussian Mixture Model (GMM) and the Universal Background Model adapted...
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