1802579
Analysis and Improvement of An Iris Identification Algorithm
Iris recognition using complex valued 2D Gabor filters received a great success in broad applications. Such method encoded the iris phase information which was extracted according to the complex valued 2D filters' response. In this paper, we theoretically analyzed the complex-valued 2D Gabor filters. Based on analysis, we concluded that the real part of complex-valued 2D Gabor filter would reduce the whole system performance because it is not perfectly band passed filter. An improved algorithm then proposed to extract the phase information of iris image. The new method has more advantages such as lower false rate, much less space of codes and shorter time of encoding and matching. In order to reduce the disturbance of eyelashes, eyelids, specular reflections, or other noise, a matching template is designed by dilating the positions of them. Experimental results show that the latter method has an encouraging performance
9214943
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.
3197098
A Multi-Objective Hw-sw Co-Synthesis Algorithm Based on Quantum-Inspired Evolutionary Algorithm
Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
26519336
Iris Recognition Algorithm Using Modified Log-Gabor Filters
In this paper, we presented an iris recognition algorithm based on modified Log-Gabor filters. The algorithm is similar as the method proposed by Daugman in general procedure while modified Log-Gabor filters are adopted to extract the iris phase information instead of complex Gabor filters used in Daugman's method. The advantage of Log-Gabor filters over complex Gabor filters is the former are strictly bandpass filters and the latter are not. The property of strictly bandpass makes the Log-Gabor filters more suitable to extract the iris phase features regardless of the background brightness. The comparison experiments between complex Gabor filters based methods and the proposed method are also presented in this paper
29843270
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
Large scale global optimization (LSGO), which is highly needed for many scientific and engineering applications, is a very important and very difficult task in optimization domain. Various algorithms have been proposed to tackle this challenging problem, but the use of estimation of distribution algorithms (EDAs) to it is rare. This chapter aims at investigating the behavior and performances of uni-variate EDAs mixed with different kernel probability densities via fitness landscape analysis. Based on the analysis, a self-adaptive uni-variate EDA with mixed kernels (MUEDA) is proposed. To assess the effectiveness and efficiency of MUEDA, function optimization tasks with dimension scaling from 30 to 1500 are adopted. Compared to the recently published LSGO algorithms, MUEDA shows excellent convergence speed, final solution quality and dimensional scalability.
483866
3D Locomotion of a Snake-like Robot Controlled by Cyclic Inhibitory CPG Model
With 3D (3-dimensional) movement's ability and rhythmic locomotion mode, a nature snake makes itself survive in rugged terrains. The rhythmic activities of most creatures are generated by the CPG (central pattern generator). Based on this fact, the sustained-type neuron has been adopted to construct a cyclic inhibitory CPG model for a snake-like robot whose joints are perpendicularly connected in series. Having compared with the sustained-type neuron and the mutual inhibitory CPG, the cyclic inhibitory CPG was proven to generate capably rhythmic output with the least number of differential equations. In this paper, we introduce the neuron network organized by the cyclic inhibitory CPGs connected in line with unilateral excitation to control the 3D locomotion of a snake-like robot, and present the necessary condition for the CPG neuron network to sustain a rhythmic output. By implementing this control architecture to a simulator with consideration of mechanical dynamics of a real snake-like robot "Perambulator", preliminary parameter setting of the CPG neuron network for its 3D locomotion is obtained. Moreover, it is shown that "Perambulator" can successfully exhibit 3D locomotion by using the output of the proposed CPG network. The obtained results have also provided a bran new approach to understand the unknown neuron network of nature snakes
5644766
Transfer learning for collaborative filtering via a rating-matrix generative model
Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.
2854723
UTDallas at TREC 2008 Blog Track
Abstract : This paper describes our participation in the 2008 TREC Blog track. Our system consists of 3 components: data preprocessing, topic retrieval, and opinion finding. In the topic retrieval task, we applied Lemur IR toolkit and used various techniques for query expansion. In the opinion finding and polarization task, we employed a feature-based classification approach. Then re-ranking was performed using a linear combination of the opinionated score and the topic relevance score. Our system achieved reasonable performance in this evaluation.
3475098
Steganalysis of LSB Greedy Embedding Algorithm for JPEG Images using Coefficient Symmetry
A recently developed LSB greedy embedding algorithm for JPEG images is capable of resisting the chi-square attack. By carefully studying the quantized DCT (discrete cosine transform) coefficients of the cover and stego images, we find that the embedding algorithm does not preserve the histogram of the DCT coefficients well. In this paper, we define a new chi-square statistic which is used to measure whether the image under scrutiny is like the cover or the stego. Our proposed steganalytic method is based on the symmetry property of the DCT coefficients in JPEG images. It can also be used in the scenario where the cover images are double JPEG compressed. The reliability of this specific steganalytic scheme depends on the embedding rate and it is influenced by the JPEG quality factor. Experimental results show that when the embedding rate exceeds half of the maximal embedding capacity, the steganographic algorithm is detectable with a very low false negative rate, whatever the quality factor is.
38250707
Minimizing Spatial Deformation Method for Online Signature Matching
Elastic matching is an important algorithm for online signature verification. Most of elastic matching algorithms are based on dynamic time warping(DTW). In this paper, we propose a new points matching algorithm for online signature verification. After resampling a signature with equidistant spacing, we describe the resampling point by its (3+1) demesional((3+1)D) information: 3D spatial information (x-axis, y-axis, time) and a feature vector. A soft-assign matrix and two special restrictions are introduced for recording the correspondence between two signatures. With a deterministic annealing framework, the mapping between two sets of (3+1)D signature points is achieved by minimizing the warping energy of the thin-plate spline(TPS). Experimental results demonstrate that the proposed algorithm outperforms the DTW-based matching algorithms.