10918777
Online Signature Verification by Combining Shape Contexts and Local Features
Most parameter-based online signature verification methods achieve correspondence between the points of two signatures by minimizing the accumulation of their local feature distances. The matching based on minimizing the local feature distances alone is not adequate since the point contains not only local features but the distribution of the remaining points relative to it. One useful way to get correspondences between points on two shapes and measure the similarity of the two shapes is to use the shape context, since this descriptor can be used to describe the distributive relationship between a reference point and the remaining points on a shape. In this paper, we introduce a shape context descriptor for describing an online signature point which contains both 2D spatial information and a time stamp. A common algorithm, dynamic time warp (DTW), is used for the elastic matching between two signatures. When combining shape contexts and local features, we achieve better results than when using only the local features. We evaluate the proposed method on a signature database from the First International Signature Verification Competition (SVC2004). Experimental results demonstrate that the shape context is a good feature and has available complementarity for describing the signature point. The best result by combining the shape contexts and the local features yields an Equal Error Rate (EER) of 6.77% for five references.
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.
40597607
Towards an Analysis of Source-Rewriting Anonymous Systems in a Lossy Environment
Anonymity that protects the identity of participants is a basic requirement for many network-based applications. For practical anonymous communication systems, strong anonymity, reliability and efficiency are three critical properties, but they may degrade in a lossy environment (nodes failure or links crash). This paper takes Crowds system as an example and statistically analyzes the degradation of these properties in lossy environment. Theoretical deduction and numerical analysis illustrate that Crowds is reliable enough to tolerate nodes failure, but both efficiency and anonymity will degrade. With the theoretical analysis results in this paper, a Crowds system operator can adjust the parameter pf to be tolerant of different node failure rate and node corruption rate.
35117974
Online signature verification based on null component analysis and principal component analysis
This paper describes a method for stroke-based online signature verification using null component analysis (NCA) and principal component analysis (PCA). After the segmentation and flexible matching of the signature, we extract stable segments from each reference signature in order that the segment sequences have the same length. The reference set of feature vectors are transformed and separated into null components (NCs) and principal components (PCs) by K-L transform. Online signature verification is a special two-category classification problem and there is not a single available forgery set in an actual system. Therefore, it is different from the typical application of PCA in pattern recognition that both NCA and PCA are used to respectively analyze stable and unstable components of genuine reference set. Experiments on a data set containing a total 1,410 signatures of 94 signers show that the NCA/PCA-based online signature verification method can achieve better results. The best result yields an equal error rate of 1.9%.
9741896
Authoritative Server's Impact on Domain Name System's Performance and Security
7181717
A Biased Support Vector Machine Approach to Web Filtering
Web filtering is an inductive process which automatically builds a filter by learning the description of user interest from a set of pre-assigned web pages, and uses the filter to assign unprocessed web pages. In web filtering, content similarity analysis is the core problem, the automatic-learning and relativity-analysis abilities of machine learning algorithms help solve the above problems and make ML useful in web filtering. While in practical applications, different filtering task implies different userinterest and thus implies different filtering result. This work studies how to adjust the web filtering results to be more fit for the user interest. The web filtering result are divided into three categories: relative pages, similar pages and homologous pages according to different user interest. A Biased Support Vector Machine (BSVM) algorithm, which imports a stimulant function, uses training examples distribution n+/n−− and a user-adaptable parameter k to deal imbalancedly different classes of the pre-assigned pages, is introduced to adjust the filtering result to be best fit for the user interest. Experiments show that BSVM can greatly improve the web filtering performance.
37867009
Spatial Clustering with Obstacles Constraints Using Ant Colony and Particle Swarm Optimization
Spatial clustering is an important research topic in Spatial Data Mining (SDM). This paper proposes an Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use improved ACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel PKSCOC based on PSO and K-Medoids to cluster spatial data with obstacles. The PKSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).
28434248
Optimized Rate Allocation for Unbalanced Multiple Description Video Coding Over Unreliable Packet Network
28324109
Precise Eye Localization with AdaBoost and Fast Radial Symmetry
28617398
An Unbalanced Multiple Description Coding Scheme for Video Transmission Over Wireless Ad Hoc Networks
26657378
Palmprint Identification using Boosting Local Binary Pattern
Local binary pattern (LBP) is a powerful texture descriptor that is gray-scale and rotation invariant according to T. Ojala et al. (2002). Because texture is one of the most clearly observable features in low-resolution palmprint images, we think local binary pattern based features are very discriminative for palmprint identification. In this paper, we propose a palmprint identification approach using boosted local binary pattern based classifiers. The palmprint area is scanned with a scalable sub-window from which local binary pattern histograms are extracted to represent the local features of a palmprint image. The multi-class problem is transformed into a two-class one of intra- and extra-class by classifying every pair of palmprint images as intra-class or extra-class ones in the work of B. Moghaddam et al. (1996). We use the AdaBoost algorithm in the work of Y. Freund and R.E. Schapire (1997) to select those sub-windows that are more discriminative for classification. Weak classifiers are constructed based on the Chi square distance between two corresponding local binary pattern histograms. Experiments on the UST-HK palmprint database show competitive performance