Showing papers in "Pattern Recognition in 2006"

TL;DR: Categorize and evaluate face recognition algorithms that rely heavily on the size and representative of training set, and the prominent algorithms are described and critically analyzed.

TL;DR: The proposed method does not require any parameter tuning by the user and can deal with degradations which occur due to shadows, non-uniform illumination, low contrast, large signal-dependent noise, smear and strain.

TL;DR: The methods for mass detection and classification for breast cancer diagnosis are discussed, and their advantages and drawbacks are compared.

TL;DR: A feature-level fusion approach for improving the efficiency of palmprint identification using multiple elliptical Gabor filters with different orientations to extract the phase information on a palmprint image, which is then merged according to a fusion rule to produce a single feature called the Fusion Code.

TL;DR: The aims of this paper are to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature.

TL;DR: In this paper, a number of experiments has been conducted on various metric and non-metric dissimilarity representations and prototype selection methods, and it is found that systematic approaches lead to better results than the random selection.

TL;DR: This work proposes an alternative to the merge-based approach to clustering by removing the clusters iteratively one by one until the desired number of clusters is reached, and applies local optimization strategy by always removing the cluster that increases the distortion the least.

TL;DR: This work presents a new heuristic to select relevant gene subsets in order to further use them for the classification task, based on the statistical significance of adding a gene from a ranked-list to the final subset.

TL;DR: A novel multi-objective evolutionary biclustering framework is introduced by incorporating local search strategies and a new quantitative measure to evaluate the goodness of the biclusters is developed.

TL;DR: This work presents an innovative method that combines a feature-based approach with a holistic one for three-dimensional (3D) face detection, which has been tested, with good results, on some 150 3D faces acquired by a laser range scanner.

TL;DR: It is found that the claim of having achieved a zero EER is based upon an impractical hidden assumption, and it is not possible to achieve the claimants' reported performance without the hidden assumption and that the results are worse than when using the biometric alone.

TL;DR: Results show the accuracy of the form for computing discrete Zernike moments and confirm that the proposed method for the fast computation of ZERNike moments is much more efficient than existing fast methods in most cases.

TL;DR: By means of the kernel functions introduced in this paper, string and graph classification can be performed in an implicit vector space using powerful statistical algorithms and can be outperformed by support vector machines using the proposed kernel functions.

TL;DR: A database of scores taken from experiments carried out on the XM2VTS face and speaker verification database is described and several fusion protocols are proposed and some state-of-the-art tools to evaluate the fusion performance are provided.

TL;DR: A refined rough cluster algorithm is presented that is applied to synthetic, forest and microarray gene expression data and successfully applied to web mining.

TL;DR: A novel feature selection method named filtered and supported sequential forward search (FS_SFS) in the context of support vector machines (SVM) is presented, which has two important properties to reduce the time of computation.

TL;DR: This paper proposes another possibilistic clustering algorithm (PCA) which is based on the FCM objective function, the partition coefficient (PC) and partition entropy (PE) validity indexes, and generalized the validity indexes of FCM.

TL;DR: This paper proposes an approach to make LLE more robust, known as robust locally linear embedding (RLLE), and presents several specific methods for realizing this general RLLE approach.

TL;DR: A first-of-its-kind comprehensive and multi-goal study, which includes investigation of eight prediction algorithms, three protein sequence representations, three datasets with different homologies and finally three test procedures, showing that sequence homology has very significant impact on the prediction accuracy.

TL;DR: A general framework to integrate a new type of constraints, based on spatial relations, in deformable models is presented and three methods to construct an external force from a fuzzy set representing a spatial relation are introduced.

TL;DR: An efficient approach for face image feature extraction, namely, (2D)^2LDA method is presented, which obtains good recognition accuracy despite having less number of coefficients.

TL;DR: A new c-colored k-out-of-n VSSS is proposed by using a pixel expansion of @?log"2c@?xm that is more efficient than ever.

TL;DR: A novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition that directly seeks the optimal projective vectors from diagonal face images without image-to-vector transformation.

TL;DR: An enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems of unstable ICA classifier and small sample size problem is proposed.

TL;DR: A novel method for breast cancer diagnosis using the feature generated by genetic programming based on Fisher criterion to transform information from high dimensional feature space into one dimensionality space and automatically discover the relationships among data, in order to improve classification accuracy.

TL;DR: In this article, the authors promote the use of statistical methods in specific classification tasks since statistical methods have certain advantages which advocate their use in pattern recognition, and propose a method for confidence estimation.

TL;DR: The experimental results show that the three principal lines and some portions of weak lines are genetically related features but the palms still contain rich genetically unrelated features for classifying identical twins.

TL;DR: This paper presents a robust rule-based approach for the splitting of binary clumps that are formed by objects of diverse shapes and sizes using a fast and accurate scheme based on concavity-based rules.

TL;DR: This paper focuses on the problem of classifying 16 types of meeting-room acoustic events and develops several classifiers based on support vector machines (SVM) using confusion matrix based clustering schemes to deal with the multi-class problem.

TL;DR: A new method for detecting and tracking multiple moving objects based on discrete wavelet transform and identifying the moving objects by their color and spatial information is proposed and the experimental results prove the feasibility and usefulness of this method.