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Highlights • A new algorithm to compute the moments defined in a rectangular region is developed. • It is verified that our computational method can efficiently and accurately compute Legendre, Gegenbauer, and Jacobi moments. • With our new algorithm, using the higher orders of moments defined in a rectangular region in real-time image analysis and recognition applications will be possible. Abstract In this research, we have developed a new algorithm to compute the moments defined in a rectangular region. By applying the recurrent formulas, symmetry properties, and particularly the parallelized matrix operations, our proposed computational method can improve the efficiency of computing Legendre, Gegenbauer, and Jacobi moments extensively with highly satisfied accuracy. To verify this new computational algorithm, the image reconstructions from the higher orders of Legendre, Gegenbauer, and Jacobi moments are performed on a testing image sized at 1024 × 1024 with very encouraging results. It took only a few seconds to compute moments and conduct the image reconstructions from the 1000-th order of the Legendre, Gegenbauer, and Jacobi moments with the PSNR values up to 45. By utilizing our new algorithm, image analysis and recognition applications using the higher orders of moments defined in a rectangular region in the range of milliseconds will be possible. [ABSTRACT FROM AUTHOR]

Abstract: A new sparsity driven kernel classifier is presented based on the minimization of a recently derived data-dependent generalization error bound. The objective function consists of the usual hinge loss function penalizing training errors and a concave penalty function of the expansion coefficients. The problem of minimizing the non-convex bound is addressed by a successive linearization approach, whereby the problem is transformed into a sequence of linear programs. The algorithm produced comparable error rates to the standard support vector machine but significantly reduced the number of support vectors and the concomitant classification time. [Copyright &y& Elsevier]

Abstract: Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named ML-KNN is presented, which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance to some well-established multi-label learning algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents a segmentation algorithm for 3D triangular mesh data. The proposed algorithm uses iterative merging of adjacent triangle pairs based on their orientations. The oversegmented regions are merged again in an iterative region merging process. Finally, the noisy boundaries of each region are refined. The boundaries of each region contain perceptually important geometric information of the entire mesh model. According to the purpose of the segmentation, the proposed mesh-segmentation algorithm supports various types of segmentation by controlling parameters. [Copyright &y& Elsevier]

Abstract: A feature selection methodology based on a novel Bhattacharyya space is presented and illustrated with a texture segmentation problem. The Bhattacharyya space is constructed from the Bhattacharyya distances of different measurements extracted with sub-band filters from training samples. The marginal distributions of the Bhattacharyya space present a sequence of the most discriminant sub-bands that can be used as a path for a wrapper algorithm. When this feature selection is used with a multiresolution classification algorithm on a standard set of texture mosaics, it produces the lowest misclassification errors reported. [Copyright &y& Elsevier]

Abstract: Unsupervised clustering is an important tool to analyze video data. Selection of an appropriate clustering scheme is governed by the suitability of the clusters it produces. It is difficult to formulate cluster suitability criteria for a domain where different feature attributes have different meanings. We propose a novel clustering strategy, tailored towards the specific requirements of clustering in video data. Our clustering methodology decouples clustering along different feature components. Our scheme chooses the clustering model so as to meet the requirements of clustering in video data. The clusters obtained from our scheme reasonably model the homogeneous color regions in a video scene in both space and time. The space–time clusters obtained by our clustering methodology can be subsequently grouped together to compose meaningful objects. Experimental comparison of our results with existing clustering techniques clearly show that our scheme takes care of many of the problems with traditional clustering schemes applied to the heterogeneous feature space of video. [Copyright &y& Elsevier]

Abstract: Applying graph theory to clustering, we propose a partitional clustering method and a clustering tendency index. No initial assumptions about the data set are requested by the method. The number of clusters and the partition that best fits the data set, are selected according to the optimal clustering tendency index value. [Copyright &y& Elsevier]

Abstract: Agglomerative clustering generates the partition hierarchically by a sequence of merge operations. We propose an alternative to the merge-based approach by removing the clusters iteratively one by one until the desired number of clusters is reached. We apply local optimization strategy by always removing the cluster that increases the distortion the least. Data structures and their update strategies are considered. The proposed algorithm is applied as a crossover method in a genetic algorithm, and compared against the best existing clustering algorithms. The proposed method provides best performance in terms of minimizing intra-cluster variance. [Copyright &y& Elsevier]

Abstract: Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real-valued static prototypes. It is shown in this paper that a continuous-time dynamical neural network model, employing a radial basis function and a sigmoid multi-layer perceptron sub-networks, is capable of maximizing such an energy form locally, thus performing almost perfectly nearest neighbor classification, when initiated by a distorted pattern. The proposed design scheme allows for explicit representation of prototype patterns as network parameters, as well as augmenting additional or forgetting existing memory patterns. The dynamical classification scheme implemented by the network eliminates all comparisons, which are the vital steps of the conventional nearest neighbor classification process. The performance of the proposed network model is demonstrated on binary and gray-scale image reconstruction applications. [Copyright &y& Elsevier]