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This paper proposes a multiple kernel construction method for kernel discriminant analysis. The constructed kernel is a linear combination of several base kernels with a constraint on their weights. By maximizing the margin maximization criterion (MMC), we present an iterative scheme for weight optimization. The experiments on several UCI real data benchmarks show that, the constructed kernel with...
In this paper, we extend the idea of sparse representation into the high dimensional feature space induced by the kernel function, and propose a kernel based test sample sparse representation and classification algorithm (KTSRC) for the first time. The KTSRC is based on the assumption that the test sample can be linearly represented by a part of the training samples in the high dimensional feature...
In this paper we present a hybrid generative-discriminative approach for image categorization in real-world images, based on Latent Dirichlet Allocation and SVM classifiers. We use SVMs with non-linear kernels on different visual features in a multiple kernel combination framework. A major contribution of our work is also the introduction of a novel dataset, called MICC-Flickr101, based on the popular...
Chemoinformatics aim to predict molecule's properties through informational methods. Computer science's research fields concerned with chemoinformatics are machine learning and graph theory. From this point of view, graph kernels provide a nice framework for combining these two fields. We present in this paper two contributions to this research field: a graph kernel based on an optimal linear combination...
Most existing feature selection methods focus on ranking individual features based on a utility criterion, which neglecting the correlations among features. To overcome this problem, we develop a novel feature selection technique using the spectral data transformation and by using l1-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique...
Emotion recognition from Speech has been a very active research topic in pattern recognition. In this paper, we investigate the use of kernel reduced-rank regression (KRRR) model to address the emotion recognition problem from speech. KRRR is a nonlinear extension of the linear reduced-rank regression (RRR) model via the kernel trick, in which a kernel mapping is used for the multivariable of RRR...
In this paper, we propose a method to recognize food images which include multiple food items considering co-occurrence statistics of food items. The proposed method employs a manifold ranking method which has been applied to image retrieval successfully in the literature. In the experiments, we prepared co-occurrence matrices of 100 food items using various kinds of data sources including Web texts,...
In this paper, a novel approach to single image super-resolution based on the multikernel regression is presented. This approach aims to learn the map between the space of high-resolution image patches and the space of blurred high-resolution image patches, which are the interpolation results generated from the corresponding low-resolution images. Kernel regression based super-resolution approaches...
Domain adaptation algorithms that handle shifts in the distribution between training and testing data are receiving much attention in computer vision. Recently, a Grassmann manifold-based domain adaptation algorithm that models the domain shift using intermediate subspaces along the geodesic connecting the source and target domains was presented in [6]. We build upon this work and propose replacing...
In order to estimate multiple structures without prior knowledge of the noise scale, this paper utilizes Jensen-Shannon Divergence (JSD), which is a similarity measurement method, to represent the relations between pairwise data conceptually. This conceptual representation encompasses the geometrical relations between pairwise data as well as the information about whether pairwise data coexist in...
We propose a new example-based video upscaling technique that exploits self-similarity among patches of a video in both space and time. We encode image patches with over-complete dictionaries constructed in a local spatio-temporal neighborhood, and establish temporal correspondence using modern optical flow techniques. The resulting method performs favorably compared to the state-of-the-art in super-resolution...
Feature redundancy and loss of local feature are central problems for image classification. Feature selection decreases the feature redundancy by choosing a subset of features and eliminating those with low prediction. The local feature representation is able to highlight objects in an image, thus, overcoming the drawbacks of global features. This paper presents a new method, called the local kernel...
The huge increase of hyperspectral data dimensionality and information redundancy has brought high computational cost as well as the over-fitting risk of classification. In this paper, we present an automatic band selection and classification method based on a novel wrapper Multiple Improved particle swarm cooperative optimization and support vector machine model (MIPSO-SVM). The MIPSO-SVM model optimizes...
In this paper, we propose a new learning paradigm named multitask multiclass privileged information support vector machines. The starting point of our work is mainly based on the success of multitask multiclass support vector machines which cast multitask multiclass problems as a constrained optimization problem with a quadratic objective function. Learning using privileged information is an advanced...
Shape analysis relies on using a finite number of points on the contour of an object to compare the shapes of objects. These points are called landmarks. Hence, when landmarks are not available for analysis, we must place some appropriately on the contour. In this paper, we describe a new method for placing landmarks well on the contours of objects in the same class. The landmarks located by our method...
In this paper we study the problem of score normalization in biometric verification systems. Specifically, we introduce a new class of normalization techniques, which unlike the commonly used parametric score normalization techniques, such as z- or t-norm, make no assumptions regarding the shape of the underlying score distribution. The proposed class of normalization techniques first estimates the...
Pedestrian detection is a key problem in many computer vision applications, especially in surveillance and security systems. To this end, information integration from different imaging modalities, such as thermal infrared and visible spectrum, can significantly improve the detection rate in respect to mono-modal strategies. For this reason, an effective fusion scheme is necessary to combine the information...
Studies on human faculties of scene recognition have lead to two broad classifications of the perceived information: local and global. It has been shown that both are processed separately and combined towards final category assignment. Recently, it was suggested that accuracy of computational models for local information closely match human performance, while it is not so for current global representations...
In this paper, we propose a compact image signature based on VLAT. Our method integrates spatial information while significantly reducing the size of original VLAT by using two pojection steps. we carry out experiments showing our approach is competitive with state of the art signatures.
The advent of inexpensive depth augmented color (RGBD) sensors has brought about a large advancement in the perceptual capability of vision systems and mobile robots. Challenging vision problems like object category, instance and pose recognition have all benefited from this recent technological advancement. In this paper we address the challenging problem of pose recognition using simultaneous color...
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