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In our previous study, we visualized microarray data of hepatocellular carcinoma (HCC) by using self-organizing-map, and investigated molecular signature representing the development of HCC. In this study, we propose two visualization methods of microarray data with Euclidean distance classifiers and Sammonpsilas nonlinear mapping. Our proposed methods will serve as tool to discover molecular signature...
Balancing the recognition rate, processing time and memory requirement is an important issue for object recognition based on local features. For the task of recognizing not generic but specific objects (object instances), a larger number of local features enable us to improve the recognition rate but pose a problem of processing time and memory requirement. For the problem of processing time, approximate...
The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. In this paper, first, a new method called clustering-based locally linear embedding (CLLE) is proposed, which is able to solve the problem of high time consuming of LLE and preserve the data topology at the same time. Then, how the proposed method achieves decreasing...
Visual dictionaries are widely employed in object recognition to map unordered bags of local region descriptors into feature vectors for image classification. Most visual dictionaries have been constructed by unsupervised clustering. This paper presents an efficient discriminative approach, called iterative discriminative clustering (IDC), for dictionary learning. In this approach, each dictionary...
In this paper, an entropic thresholding method based on the gray-level spatial correlation (GLSC) histogram defined by ourselves is presented. Compared with traditional two-dimensional histogram, we take into account the image local property in a different way by GLSC histogram. In experiment, we make comparison of the proposed method with two-dimensional entropic thresholding method proposed by Abutaleb...
Recently, Viola proposed a rectangular features (RFs) based classifier with high accuracy and rapid processing speed for object detection tasks. In this paper, we propose non-neighboring RFs (NNRFs) as an extension of RFs, and a particle swarm optimization (PSO) based feature selection algorithm for NNRFs. NNRFs are the pairs of arbitrary rectangular sub-regions in images, giving us huge number of...
In this paper, a novel class-dependence feature analysis method based on Correlation Filter Bank (CFB) technique for effective multimodal biometrics fusion at the feature level is developed. In CFB, the unconstrained correlation filter trained for a specific modality is designed by optimizing the overall original correlation outputs. Therefore, the differences between modalities have been taken into...
Shape-from-shading methods recover 3-D shape from intensity images. Often, Lambertian reflectance is assumed. The Lambertian assumption is attractive because it simplifies the analysis. Alternatively, non-Lambertian reflectance, including specularity, is accommodated in methods that measure reflectance empirically either using a separate calibration object or the target object itself, in self-calibration...
In this paper, the matching of SIFT-like features [5] between images is studied. The goal is to decide which matches between descriptors of two datasets should be selected. This matching procedure is often a preliminary step towards some computer vision applications, such as object detection and image registration for instance. The distances between the query descriptors and the database candidates...
Application-specific dissimilarity functions can be used for learning from a set of objects represented by pairwise dissimilarity matrices in this context. These dissimilarities may, however, suffer from various defects, e.g. when derived from a suboptimal optimization or by the use of non-metric or noisy measures. In this paper, we study procedures for refining such dissimilarities. These methods...
Trifocal tensor encapsulates the geometric constraints between three views. It plays an important role in computer vision. However elements in measurement matrix of existing linear trifocal tensor estimation algorithms are products of several measurement data, which can amplify measurement error. The factorization algorithm of trifocal tensor estimation is presented, which can overcome this shortcoming...
With the increase of the training set??s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result. We adopt a non-parametric k-NN rule called relative neighborhood graph...
This work presents an award winning approach for solving the NN3 forecasting competition problem. It consisted of predicting 18 future values of 111 monthly short time series. This approach consists of applying the median value of a 15-MLP ensemble for predicting each time series. The system performed very well on test data, finishing as the second best solution of the competition with a SMAPE=16...
Impression evidence in the form of shoe-prints are commonly found in crime scenes. A critical step in automatic shoe-print identification is extraction of the shoe-print pattern. It involves isolating the shoe-print foreground (impressions made by the shoe) from the remaining elements (background and noise). The problem is formulated as one of labeling the regions of a shoeprint image as foreground/background...
Biometrics based on electroencephalogram (EEG) signals is an emerging research topic. Several recent results have shown its feasibility and potential for personal identification. However, they all use a single task (e.g., signals recorded during imagination of repetitive left hand movements or during resting with eyes open) for classifier design and subsequent identification. In contrast with this,...
Epilepsy is a neurological disorder which causes two million people in the United States for suffering. In this research, we proposed a seizure detection method based on intracranial electroencephalogram (IEEG) by using signal processing and intelligence computing approach, which combines filtering, back propagation neural network (BPNN), multi-resolution Teager energy operator (MTEO), smooth window,...
Email is a commonly used tool for communication which allows rapid and asynchronous communication. The growing popularity and low cost of e-mails have made spamming an extremely serious problem today. Several anti-spam filtering techniques have been developed but most of them suffer from low accuracy and high false alarm rate due to complexity and changing nature of unsolicited messages. This study...
We consider the problem of classification when multiple observations of a pattern are available, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all the unlabelled samples belong to the same unknown class. We build on graph-based methods for semi-supervised learning and we optimize the graph construction in order to exploit the special...
In this paper, we unify C1 units and the locality preserving projections (LPP) into the conventional gist model for scene classification. For the improved gist model, we first utilize the C1 units, intensity channel and color channel of color image to represent the color image with the high dimensional feature, then we project high dimensional samples to a low dimensional subspace via LPP to preserve...
Creases, as a special salient feature of palmprint, are large in number and distributed at all directions. It changes slowly in a personpsilas whole life, which qualifies themselves as features in palmprint identification. In this paper, we devised a new algorithm of crease extraction by using non-separable bivariate wavelet filter banks with linear phase. Compared with the traditional wavelet, our...
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