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The aim of this work is to propose four methods for composer classification in symbolic data based on melodies making use of the Prediction by Partial Matching (PPM) algorithm, and also to propose data modeling inspired on psycho physiological aspects. Rhythmic and melodic elements are combined instead of using only melody or rhythm alone. The models consider the perception of pitch changing and note...
This paper presents BIGS the Big Image Data Analysis Toolkit, a software framework for large scale image processing and analysis over heterogeneous computing resources, such as those available in clouds, grids, computer clusters or throughout scattered computer resources (desktops, labs) in an opportunistic manner. Through BIGS, eScience for image processing and analysis is conceived to exploit coarse...
One of the first steps in most facial expression and facial analysis systems is the localization of prominent facial feature points. In this paper we present a novel approach for facial feature point detection using Simplified Gabor Wavelets (SGW). The classifier is built in cascades, where each stage of the cascade is a Gentle-AdaBoost trained classifier. In addition, we suggest a confidence based...
In this paper, a k-nearest neighbor locally weighted regression method (k-LWR) is proposed to forecast the short-term traffic flow. Inspired by k-nearest neighbor (k-NN) method, the traffic flows which have the same clock time with the current traffic flow are viewed as neighbors. The traffic flows which have the same clock time with the predicted traffic flow are viewed as the outputs of the neighbors...
In this paper, we propose a very simple face recognition method. This method first exploits a linear combination of all the training samples to express the test sample. Then it evaluates the capability of each class in expressing the test sample and assigns the test sample to the class that has the strongest capability. Using the expression result, the proposed method can classify the testing sample...
This paper proposes a neural network based framework to classify online Devanagari characters into one of 46 characters in the alphabet set. The uniqueness of this work is three-fold: (1) The feature extraction is just the Discrete Cosine Transform of the temporal sequence of the character points (utilizing the nature of online data input). We show that if used right, a simple feature set yielded...
The multimodal biometric systems are gaining popularity because of accurate and reliable identification of the person. In this paper, we present a novel weighting scheme using variants of Particle Swarm Optimization (PSO) for efficient feature level fusion of face and palmprint. The face and palmprint images are represented using Log Gabor features which are then concatenated to form a fused feature...
Machine vision is applied to detect wood knots and cracks, to classify strong and stable woods. In order to obtain effective and efficient classification a well-defined pattern recognition and feature extraction algorithms are essential. In this paper we examine three different methods for feature extraction; Gray level co-occurrence matrix (GLCM), Local binary patterns (LBP), and statistical moments...
As a powerful algorithm for face recognition, the proposed Two-Phase Test Sample Representation (TPTSR) increases the classification rate by dividing the recognition task into two steps. The first step intends to find the M most possible candidate training samples from the whole training set to match with the testing input, and the second phase classifies the testing sample to the class with the most...
Most existing cancelable biometrie frameworks are based on one-dimensional (ID) vectors rather than two-dimensional (2D) images or feature matrices. 2D cancelable biometrics, generated directly from images of feature matrices, were proposed based on two-directional two-dimensional fusion sparse random projection ((2D)2FSRP) and two-directional two-dimensional plus sparse random projection ((2D)2PSRP),...
Individual recognition is the technique which recognizes person's identity through his gait. Gait energy image (GEI) is a classical gait representation and it can be decomposed into structural part and detailed part. Then virtual gait energy image (VGEI) can be constructed in virtual space by integrating those two different parts. The generalized principal component analysis (GPCA) is applied to VGEI...
We present a new gait identification method based on dynamic time warping (DTW), as video surveillance system requires high accuracy and precision. It could reduce computational cost of gait recognition, significantly improve the recognition rate for gait and meet the demand of video surveillance. The characters of human appearance have been utilized to extract entire binary image of human silhouette...
Support vector machines (SVMs) have become an alternative tool for pattern recognitions, and more specifically for Handwritten Signature Verification Systems (HSVS). Usually, the bi-class SVMs (B-SVM) are used for separating between genuine and forged signatures. However, in practice, only genuine signatures are available. In this paper, we investigate the use of one-class SVM (OC-SVM) for handwritten...
Pattern identification algorithms can be exploited for single-class or multi-class identification problems. In conventional applications, identification algorithms are based on functions of measured features. However in some cases, we already have an identification algorithm but the measured features are different from the features expected by the algorithm. In such cases, we can first estimate the...
In this paper, a new age estimation framework considering the intrinsic properties of human ages is proposed, which improves the dimensionality reduction techniques to learn the connections between facial features and aging labels. To enhance the performance of dimensionality reduction, a distance metric adjustment step is introduced in advance to achieve a suitable metric in the feature space. In...
In this paper, we present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. Themain feature of the proposed approach is that it provides rotation invariant clustering which is useful in Content Based Image Retrieval (CBIR). We demonstrate through experimental...
Forensic palmprint recognition, which mainly deals with high-resolution palmprints and latent-to-full palmprint comparison, has aroused research highlights because of the increased use of the evidence of palmprints in forensics. There are some in-depth works on high-resolution palmprint preprocessing (i.e., segmentation and enhancement) and feature extraction. However, few works on latent-to-full...
We introduce a novel subspace segmentation method called Minimal Squared Frobenius Norm Representation (MSFNR). MSFNR performs data clustering by solving a convex optimization problem. We theoretically prove that in the noiseless case, MSFNR is equivalent to the classical Factorization approach and always classifies data correctly. In the noisy case, we show that on both synthetic and real-word datasets,...
This paper delves into the effectiveness of a gait recognition process depending on the length of the video sequence used. To this end, a well-known gait representation, the Gait Energy Image (GEI), is incrementally computed from gait cycles in the order they occur. The main objective is to assess the problem of the minimum number of gait cycles required to obtain discriminant GEIs. An experimental...
In this article, we present a novel set of features for detection of text in images of natural scenes using a multi-layer perceptron (MLP) classifier. An estimate of the uniformity in stroke thickness is one of our features and we obtain the same using only a subset of the distance transform values of the concerned region. Estimation of the uniformity in stroke thickness on the basis of sparse sampling...
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