Showing papers in "Pattern Recognition in 2013"

TL;DR: The results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices.

TL;DR: A methodology to compare the performance of different focus measure operators for shape-from-focus is presented and applied and the selected operators have been chosen from an extensive review of the state-of-the-art.

TL;DR: The proposed method for automatically extracting blood vessels from colour retinal images is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved and it produces accurate segmentation on central reflex vessels while keeping close vessels well separated.

TL;DR: Experimental results demonstrate that the novel color difference histograms (CDH) method is much more efficient than the existing image feature descriptors that were originally developed for content-based image retrieval, such as MPEG-7 edge histogram descriptors, color autocorrelograms and multi-texton histograms.

TL;DR: This paper develops a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting algorithm, and proves that EUSBoost is able to outperform the state-of-the-art methods based on ensembles.

TL;DR: A systematic survey of graph theoretical methods for image segmentation, where the problem is modeled in terms of partitioning a graph into several sub-graphs such that each of them represents a meaningful object of interest in the image.

TL;DR: This work proposes the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample, and introduces the prior weight of each basis during sparse reconstruction, which is more robust than other outlier detection criteria.

TL;DR: This paper first analyzes the benefits of using sparse coding in visual tracking and then categorizes these methods into appearance modeling based on sparse coding (AMSC) and target searchingbased on sparse representation (TSSR) as well as their combination.

TL;DR: In this paper, state-of-the-art methods were evaluated on the isolated character datasets OLHWDB1.0 and HWDB-1.1 for Chinese handwriting recognition.

TL;DR: A new robust twin support vector machine (called R-TWSVM) via second order cone programming formulations for classification, which can deal with data with measurement noise efficiently and successfully overcomes the existing shortcomings of TWSVM is proposed.

TL;DR: The WMIL tracker integrates the sample importance into an efficient online learning procedure by assuming the most important sample is known when training the classifier, leading to a more robust and much faster tracker.

TL;DR: In this paper, an enhanced regularized random forest (RRF) is proposed, referred to as the guided RRF (GRRF), where the importance scores from an ordinary random forest are used to guide the feature selection process in RRF.

TL;DR: This paper develops methods for learning two types of ensembles (bagging and random forests) of predictive clustering trees for global and local predictions of different types of structured outputs, and proposes to build ensemble models consisting of predictive clustered trees, which generalize classification trees.

TL;DR: A hybrid classifier which combines the Gaussian mixture model (GMM), support vector machine (SVM), and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification is presented.

TL;DR: An automatic stopping criterion is proposed, that takes into consideration the quality of the preserved edges as opposed to just the level of smoothing achieved, and is compared with other state of the art schemes using objective criteria.

TL;DR: This paper proposes a sparse approximation to a robust vector field learning method, sparse vector field consensus (SparseVFC), and derives a statistical learning bound on the speed of the convergence, and applies SparseVFC to the mismatch removal problem.

TL;DR: Wang et al. as discussed by the authors provided a comparative study of 12 representative illumination preprocessing methods (HE, LT, GIC, DGD, LoG, SSR, GHP, SQI, LDCT, LTV and TT) from two novel perspectives: localization for holistic approach and integration of large-scale and small-scale feature bands.

TL;DR: A new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, is developed to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures.

TL;DR: This paper combines distance metric learning and dimensionality reduction to better explore the connections between facial features and age labels and presents an age-oriented local regression to capture the complicated facial aging process for age determination.

TL;DR: This paper proposes to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition that outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).

TL;DR: This article investigates the use of Gaussian process (GP) priors for one-class classification and shows the suitability of the methods in the area of attribute prediction, defect localization, bacteria recognition, and background subtraction.

TL;DR: It is shown that with Gabor feature transformation, l"2-norm could take the role of l"1-norm to regularize the coding coefficients, which reduces significantly the computational cost in coding occluded face images.

TL;DR: An extensive study of the behavior of OCRF is proposed, that includes experiments on various UCI public datasets and comparison to reference one class namely, Gaussian density models, Parzen estimators,Gaussian mixture models and One Class SVMs-with statistical significance.

TL;DR: The results show that the proposed ellipse fitting method is quite selective to elliptic shapes only and provides accurate fitting results, indicating potential application in medical, robotics, object detection, and other image processing industrial applications.

TL;DR: This work proposes a real-time, parameter-free circle detection algorithm that has high detection rates, produces accurate results and controls the number of false circle detections, and makes use of the contiguous set of edge segments produced by the Edge Drawing Parameter Free (EDPF), hence the name EDCircles.

TL;DR: A stratified sampling method to select the feature subspaces for random forests with high dimensional data to better that of state-of-the-art algorithms including SVM, the four variants of random forests, and nearest neighbor algorithms.

TL;DR: This paper presents a simple but effective scene classification approach based on the incorporation of a multi-resolution representation into a bag-of-features model and shows that the proposed approach performs competitively against previous methods across all data sets.

TL;DR: Noise removal and vessel localization are achieved by a multiscale hierarchical decomposition of the normalized enhanced image, and a binary map of the vasculature is obtained by locally adaptive thresholding, generating a threshold surface based on the vessel edge information extracted by the previous processes.

TL;DR: Evaluation on a set of 129 CT lung tumor images using a similarity index was done, and the newly developed automatic lung lesion segmentation algorithm is stable, accurate and automated.

TL;DR: A comprehensive overview of the different proposals for edge detection performance measures is made, followed by a practical comparison of the most representative measures on synthetic as well as natural edge images.