Showing papers in "Pattern Recognition in 2011"

TL;DR: A survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system, the choice of suitable features for speech representation, and the proper preparation of an emotional speech database for evaluating system performance are addressed.

TL;DR: A global technique for averaging a set of sequences is developed, which avoids using iterative pairwise averaging and is thus insensitive to ordering effects, and a new strategy to reduce the length of the resulting average sequence is described.

TL;DR: This work develops a double study, using different base classifiers in order to observe the suitability and potential of each combination within each classifier, and compares the performance of these ensemble techniques with the classifiers' themselves.

TL;DR: Random forests has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection, and results of new tests regarding variable rankings based on RF variable importance measures are presented.

TL;DR: A novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW that penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers is proposed.

TL;DR: A novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method that can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions is presented.

TL;DR: This paper presents a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations, and demonstrates the effectiveness of this method in providing a reliable estimation of the defocus map.

TL;DR: The central issues of MSL are discussed, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, and examining the algorithmic aspects of typical MSL solutions.

TL;DR: A new human face recognition algorithm based on bidirectional two dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) and a subband that exhibits a maximum standard deviation is dimensionally reduced using an improved dimensionality reduction technique.

TL;DR: This work proposes ''supervised principal component analysis (supervised PCA)'', a generalization of PCA that is uniquely effective for regression and classification problems with high-dimensional input data and shows significant improvement over other supervised approaches both in accuracy and computational efficiency.

TL;DR: The results demonstrate that the proposed micro-structure descriptor is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.

TL;DR: A new age estimation method using a hierarchical classifier method based on both global and local facial features is proposed, which was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.

TL;DR: The experimental results show that the proposed gait representation-gait flow image GFI is stronger in resisting the difference of the carrying condition compared with other gait representations.

TL;DR: Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm.

TL;DR: A method for conducting off-line handwritten signature verification works at the global image level and measures the grey level variations in the image using statistical texture features using the co-occurrence matrix and local binary pattern.

TL;DR: The experimental results demonstrate that the proposed local-global information combination scheme could significantly improve the recognition accuracy obtained by either local or global information and lead to promising performance of an FKP-based personal authentication system.

TL;DR: The aim of this article is to thoroughly evaluate and categorise the most relevant algorithms with respect to the modality behind the integration of these two fundamental image attributes.

TL;DR: The conventional local binary pattern is extended to pyramid transform domain (PLBP) by cascading the LBP information of hierarchical spatial pyramids, PLBP descriptors take texture resolution variations into account and show their effectiveness for texture representation.

TL;DR: The experimental results on several artificial and benchmark datasets indicate that the novel twin parametric-margin support vector machine (TPMSVM) not only obtains fast learning speed, but also shows good generalization.

TL;DR: Zhang et al. as discussed by the authors proposed a l"1-l"0 minimization approach, where the l" 1 term is used for impulse denoising and the l' 0 term was used for sparse representation over a dictionary of images patches.

TL;DR: A Multi-Manifold Discriminant Analysis method based on graph embedded learning and under the Fisher discriminant analysis framework is proposed, leading to promising image recognition performance.

TL;DR: The results obtained indicate that the full vector of class supports should be used for evaluating the classifier competence as this potentially improves performance of MCSs.

TL;DR: A theoretic framework based on rough set theory, which is called positive approximation and can be used to accelerate a heuristic process for feature selection from incomplete data is introduced and several modified representative heuristic feature selection algorithms in roughSet theory are obtained.

TL;DR: A novel binary classifier coined projection twin support vector machine (PTSVM) is proposed to seek two projection directions, one for each class, such that the projected samples of one class are well separated from those of the other class in its respective subspace.

TL;DR: In this article, the authors proposed an online kernel density estimation (KDE) method, which maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated.

TL;DR: Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

TL;DR: A review of the use of graph representations in document analysis and a number of novel approaches suitable for making tools from statistical pattern recognition available to graphs, including graph kernels and graph embedding are discussed.

TL;DR: The experiments show that the proposed HMC system outperforms the best-performing approach from the literature (a collection of SVMs, each predicting one label at the lowest level of the hierarchy), both in terms of error and efficiency.

TL;DR: The conclusion is that the necessary technology including standard media formats for 3D video and free viewpoint video is available or will be available in the future, and that there is a clear demand from industry and user for such advanced types of visual media.

TL;DR: A pair-polar coordinate-based template design method is developed which satisfies the template protection requirements and the performance degradation caused by the transformation is very low.