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Video summarization is an efficient and flexible way to represent video data. In this paper, we use the kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the...
Compact, robust and meaningful representations of face images are crucial to the performance of any face recognition system. In this paper, we present a novel method for robust face representation based on Probability Latent Semantic Analysis (PLSA), a generative model originated from the field of text-processing. Specifically, each of face images is treated as a document consisted of a bag of visual...
Texture synthesis and morphing are important techniques for efficiently creating realistic and visually attractive textures. A popular class of synthesis algorithm are pixel-based techniques, which search in a given 2D exemplar for a pixel with a similar neighbourhood to the pixel currently being generated. The methods have the advantage that they are fast, they can be easily generalised to higher...
Statistical shape model (SSM) is to model the shape variation of an object. The statistical shape models are constructed by analysis of the positions of a set of landmark points based and use the surface information. In this paper, we propose a new PCA based statistical shape modeling technique and its application to medical applications. In the proposed method, boundary points of each slice are used...
In this paper, a infrared face recognition method using radiant energy conversion and curvelet transformation is proposed. Firstly, to get the stable feature of thermal face, thermal images are converted into radiant energy images according to Stefan-Boltzmann's law. Secondly, curvelet transform has better directional and edge representation abilities than widely used wavelet transformation and other...
This paper proposes a framework to integrate spatial information into unsupervised feature extraction for hyperspectral images. In this approach a nonlinear scale-space representation using morphological levelings is formulated. In order to apply feature extraction, tensor principal components are computed involving spatial and spectral information. The proposed method has shown significant gain over...
In this paper, we adopt constrained relaxation for distributed multi-view video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate inter-view correlated side information without knowing the camera parameters. Moreover, graph-based representations of multi-view images are incorporated to form more distinctive feature constraints. The sparse data as a...
This paper proposes a method for the identification of individuals from their gait using fuzzy logic. Gait signature is first extracted in the form of a spatiotemporal representation called Gait Energy Image (GEI). Since the dimension of GEI is very high, we use fuzzy principal component analysis (FPCA) for dimension reduction. Unlike traditional PCA, it helps to get rid of the problems of outliers...
In this paper, the algorithm for 2D shape matching and retrieval is developed by using Fisher Barycenter Contour (FBcC). First, the shape is represented into 3D format using the signed enclosed area at each scale level of Barycenter Contour (BcC). Because of high dimension of the feature representation, the eigen Barycenter Contour (EBcC) is applied for dimensionality reduction. Then, the Fisher Barycenter...
This paper presents a novel face recognition method based on the contourlet for facial features representation and using an new kernel based algorithm, for discriminating purposes, namely kernel relevance weighted discriminant analysis (KRWDA). This nonlinear reduction dimension algorithm has several interesting characteristics. First, using kernel theory, it handles nonlinearity efficiently. Second,...
The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale invariant feature transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-nearest neighbour). In this paper, we propose the dimensionality...
Outliers are important features that are of special interest to image analysts in their work. The objective of this paper is to show how several statistical techniques with different theoretical foundations can be successfully applied complementarily to detect anomalies in hyperspectral imageries. The methodology is shown in airborne hyperspectral imagery with 60 bands. The visual inspection of the...
Obtaining invariant representation of time varying signals is one of the major problems in object recognition. Recently, a new method that slowly feature analysis (SFA) which can extract invariant features of temporally varying signals is being explored, which is an extension of independent component analysis (ICA) which has been used for extracting facial feature. The technique of SFA can be extended...
A new gait method using the periodic sequence width images and kernel based Fisher discriminant analysis is proposed. The gait pattern is described by the periodic sequence width images. It exacts from the width temporal image generated by calculating the width vector sequences and representing the width value in grey level. The periodic sequence width images capture both the shape structure information...
In this paper, we propose sparse non-negative pattern learning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximation with sparseness constraints, and then features are extracted by the second part of the algorithm, a conjugate family based non-negative sparse feature extraction method. By combining...
In this paper, we propose a novel approach for palmprint identification, which contains two interesting components. Firstly, we propose the directional representation for appearance based approaches. The new representation is robust to drastic illumination changes and preserves important discriminative information for classification. We then generate virtual samples to enlarge the training set to...
In this paper we propose features based on sub-space projection methods using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) on wavelet sub-band for face recognition. Wavelet based sub-band decomposition helps to reduce the size of image, and the approximate image obtained in the low-low (approximate) band is used here to apply sub-space projection methods. This improves...
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