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Abstract: Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site . [Copyright &y& Elsevier]

Classification is an essential task for predicting the class values of new instances. Both k -fold and leave-one-out cross validation are very popular for evaluating the performance of classification algorithms. Many data mining literatures introduce the operations for these two kinds of cross validation and the statistical methods that can be used to analyze the resulting accuracies of algorithms, while those contents are generally not all consistent. Analysts can therefore be confused in performing a cross validation procedure. In this paper, the independence assumptions in cross validation are introduced, and the circumstances that satisfy the assumptions are also addressed. The independence assumptions are then used to derive the sampling distributions of the point estimators for k -fold and leave-one-out cross validation. The cross validation procedure to have such sampling distributions is discussed to provide new insights in evaluating the performance of classification algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we consider the problem of joint segmentation and classification of sequences in the framework of conditional random field (CRF) models. To effect this goal, we introduce a novel dual-functionality CRF model: on the first level, the proposed model conducts sequence segmentation, whereas, on the second level, the whole observed sequences are classified into one of the available learned classes. These two procedures are conducted in a joint, synergetic fashion, thus optimally exploiting the information contained in the used model training sequences. Model training is conducted by means of an efficient likelihood maximization algorithm, and inference is based on the familiar Viterbi algorithm. We evaluate the efficacy of our approach considering a real-world application, and we compare its performance to popular alternatives. [Copyright &y& Elsevier]

Abstract: Recently, the Student''s t-factor mixture analyzer (tFMA) has been proposed. Compared with the mixture of Student''s t-factor analyzers (MtFA), the tFMA has better performance when processing high-dimensional data. Moreover, the factors estimated by the tFMA can be visualized in a low-dimensional latent space, which is not shared by the MtFA. However, as the tFMA belongs to finite mixtures and the related parameter estimation method is based on the maximum likelihood criterion, it could not automatically determine the appropriate model complexity according to the observed data, leading to overfitting. In this paper, we propose an infinite Student''s t-factor mixture analyzer (itFMA) to handle this issue. The itFMA is based on the nonparametric Bayesian statistics which assumes infinite number of mixing components in advance, and automatically determines the proper number of components after observing the high-dimensional data. Moreover, we derive an efficient variational inference algorithm for the itFMA. The proposed itFMA and the related variational inference algorithm are used to cluster and classify high-dimensional data. Experimental results of some applications show that the itFMA has good generalization capacity, offering a more robust and powerful performance than other competing approaches. [Copyright &y& Elsevier]

Abstract: For classification problems, in practice, real-world data may suffer from two types of noise, attribute noise and class noise. It is the key for improving recognition performance to remove as much of their adverse effects as possible. In this paper, a formalism algorithm is proposed for classification problems with class noise, which is more challenging than those with attribute noise. The proposed formalism algorithm is based on evidential reasoning theory which is a powerful tool to deal with uncertain information in multiple attribute decision analysis and many other areas. Thus, it may be more effective alternative to handle noisy label information. And then a specific algorithm—Evidential Reasoning based Classification algorithm (ERC) is derived to recognize human faces under class noise conditions. The proposed ERC algorithm is extensively evaluated on five publicly available face databases with class noise and yields good performance. [Copyright &y& Elsevier]

Abstract: The Pap smear test is a manual screening procedure that is used to detect precancerous changes in cervical cells based on color and shape properties of their nuclei and cytoplasms. Automating this procedure is still an open problem due to the complexities of cell structures. In this paper, we propose an unsupervised approach for the segmentation and classification of cervical cells. The segmentation process involves automatic thresholding to separate the cell regions from the background, a multi-scale hierarchical segmentation algorithm to partition these regions based on homogeneity and circularity, and a binary classifier to finalize the separation of nuclei from cytoplasm within the cell regions. Classification is posed as a grouping problem by ranking the cells based on their feature characteristics modeling abnormality degrees. The proposed procedure constructs a tree using hierarchical clustering, and then arranges the cells in a linear order by using an optimal leaf ordering algorithm that maximizes the similarity of adjacent leaves without any requirement for training examples or parameter adjustment. Performance evaluation using two data sets show the effectiveness of the proposed approach in images having inconsistent staining, poor contrast, and overlapping cells. [Copyright &y& Elsevier]

Abstract: Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an invaluable source of information regarding the physical nature of the different materials, leading to the potential of a more accurate classification. However, high dimensionality of hyperspectral data, usually coupled with limited reference data available, limits the performances of supervised classification techniques. The commonly used pixel-wise classification lacks information about spatial structures of the image. In order to increase classification performances, integration of spatial information into the classification process is needed. In this paper, we propose to extend the watershed segmentation algorithm for hyperspectral images, in order to define information about spatial structures. In particular, several approaches to compute a one-band gradient function from hyperspectral images are proposed and investigated. The accuracy of the watershed algorithms is demonstrated by the further incorporation of the segmentation maps into a classifier. A new spectral–spatial classification scheme for hyperspectral images is proposed, based on the pixel-wise Support Vector Machines classification, followed by majority voting within the watershed regions. Experimental segmentation and classification results are presented on two hyperspectral images. It is shown in experiments that when the number of spectral bands increases, the feature extraction and the use of multidimensional gradients appear to be preferable to the use of vectorial gradients. The integration of the spatial information from the watershed segmentation in the hyperspectral image classifier improves the classification accuracies and provides classification maps with more homogeneous regions, compared to pixel-wise classification and previously proposed spectral–spatial classification techniques. The developed method is especially suitable for classifying images with large spatial structures. [Copyright &y& Elsevier]