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In this paper, the higher-order random projection (HORP) is proposed to directly project the higher-order tensor object from high-dimensional space to low-dimensional space for recognition task. In traditional random projection framework, the projection matrix does not depend on the training data hence it can avoid the principal classification problems such as over-fitting, Small Sample Size (SSS),...
A critical issue of measuring video similarity is most video data are huge files, which vary in terms of length and amount of data, resulting in time-consuming data processing. Therefore, reducing the dimensionality of the data becomes a necessity. This paper proposes the video similarity measurement approach for sports video classification by dimensionality reduction with distance space and random...
This paper proposes a new face hallucination technique for color face image reconstruction using Eigentransformation with error regression model. Generally, a high-resolution (HR) face image is reconstructed only from low-resolution (LR) face image. Nevertheless, previous researches neglect to gain benefit from error of face reconstruction. Therefore, in order to improve the performance of facial...
In this paper, we propose a new melody contour extraction technique to improve Query-by-Humming. A critical issue of humming sound is noise interference from both environment and acquisition instrument. Furthermore most users are not professional singers so they cause the other query problems about variation of pitch and timing. Advantage of a proposed technique can reduce noise whereas makes pitch...
In this paper, we propose a new super-resolution face hallucination method based on Bilateral-projection-based Two-Dimensional Principal Component Analysis (B2DPCA). Firstly, the high-resolution (HR) face image and its corresponding low-resolution (LR) face image are projected to the HR and LR B2DPCA feature spaces, respectively. In these spaces, the linear mixing relationship between HR and LR feature...
In two-dimensional principal component analysis (2DPCA), 2D face image matrices do not need to be previously transformed into a vector. In this way, the image covariance matrix can be better estimated, compared to the old fashion. The feature is derived from eigenvectors corresponding to the largest eigenvalues of the image covariance matrix for data of all classes. Normally, the number of the largest...
In this paper, we proposed a novel technique for face recognition using Two-Dimensional Random Subspace Analysis (2DRSA), based on the Two-Dimensional Principal Component Analysis (2DPCA) technique and Random Subspace Method (RSM). In conventional 2DPCA, the image covariance matrix is directly calculated via 2D images in matrix form, by concept of the covariance of a random variable. However, 2DPCA...
In this paper, we proposed a new two-dimensional linear discriminant analysis (2DLDA) method. Based on two-dimensional principle component analysis (2DPCA), face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the small sample size (SSS) problem, thus overcoming the traditional LDA...
In this paper, we proposed a class-specific subspace-based two-dimensional principal component analysis (2DPCA) for face recognition. In 2DPCA, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, 2DPCA can achieve higher performance than PCA both in face recognition and face representation task. However, both...
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