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Sport type classification and posture identification based on visual meaning of posture semantic in still images are challenging tasks. The difficulty of these tasks comes from the complex image content consisting of a player's posture, the color and texture of a player's clothes as well as complexity of the background. Player detection is one of the most important tasks in posture identification. For sport type classification without object segmentation, the new set of features, based on 64-bins color histogram, DCT coefficients, and Cb and Cr components, is introduced. To achieve high accuracy, an appropriate feature extraction technique should be also realized. For posture identification, three algorithms, concerning player region detection and suitable features for posture identification, are proposed namely blurred background elimination, irrelevant region elimination, and trimming players region. The DFT coefficients, based on image resizing and slicing techniques, are used as significant features in posture identification. Our proposed features were compared with Edge Histogram and Region-based Shape (EH and RS), two of MPEG-7 descriptors. The experimental results showed that our proposed features yielded better performance with 85.76% of accuracy in sport classification and 86.66% of accuracy in posture identification. [ABSTRACT FROM AUTHOR]

Existing clustering algorithms, such as single-link clustering, k-means, CURE, and CSM are designed to find clusters based on predefined parameters specified by users. These algorithms may be unsuccessful if the choice of parameters is inappropriate with respect to the data set being clustered. Most of these algorithms work very well for compact and hyper-spherical clusters. In this paper, a new hybrid clustering algorithm called Self-Partition and Self-Merging (SPSM) is proposed. The SPSM algorithm partitions the input data set into several subclusters in the first phase and, then, removes the noisy data in the second phase. In the third phase, the normal subclusters are continuously merged to form the larger clusters based on the inter-cluster distance and intra-cluster distance criteria. From the experimental results, the SPSM algorithm is very efficient to handle the noisy data set, and to cluster the data sets of arbitrary shapes of different density. Several examples for color image show the versatility of the proposed method and compare with results described in the literature for the same images. The computational complexity of the SPSM algorithm is O(N2), where N is the number of data points. [ABSTRACT FROM AUTHOR]

This paper demonstrates how the p-recursive piecewise polynomial ( p-RPP) generators and their derivatives are constructed. The feedforward computational time of a multilayer feedforward network can be reduced by using these functions as the activation functions. Three modifications of training algorithms are proposed. First, we use the modified error function so that the sigmoid prime factor for the updating rule of the output units is eliminated. Second, we normalize the input patterns in order to balance the dynamic range of the inputs. And third, we add a new penalty function to the hidden layer to get the anti-Hebbian rules in providing information when the activation functions have zero sigmoid prime factor. The three modifications are combined with two versions of Rprop (Resilient propagation) algorithm. The proposed procedures achieved excellent results without the need for careful selection of the training parameters. Not only the algorithm but also the shape of the activation function has important influence on the training performance. [ABSTRACT FROM AUTHOR]

Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. This makes the blind source separation infeasible and not realisable on VLSI level, due to a large memory requirement and costly computation. This paper concerns the algorithms for solving the problem of tremendous data sets and high computational complexity, so that the algorithms could be run on-line and implementable on VLSI level with acceptable accuracy. Our approach is to partition the observed signals into several parts and to extract the partitioned observations with a simple activation function performing only the "shift-and-add" micro-operation. No division, multiplication and exponential operations are needed. Moreover, obtaining an optimal initial de-mixing weight matrix for speeding up the separating time will be also presented. The proposed algorithm is tested on some benchmarks available online. The experimental results show that our solution provides comparable efficiency with other approaches, but lower space and time complexity. [ABSTRACT FROM AUTHOR]

Presently, the amount of data occurring in several business and academic areas such as ATM transactions, web searches, and sensor data are tremendously and continuously increased. Classifying as well as recognizing patterns among these data in a limited memory space complexity are very challenging. Various incremental learning methods have proposed to achieve highly accurate results but both already learned data and new incoming data must be retained throughout the learning process, causing high space and time complexities. In this paper, a new neural learning method based on radial-shaped function and discard-after-learn concept in the data streaming environment was proposed to reduce the space and time complexities. The experimental results showed that the proposed method used 1 to 95 times fewer neurons and 1.2 to 2,700 times faster than the results produced by MLP, RBF, SVM, VEBF, ILVQ, ASC, and other incremental learning methods. It is also robust to the incoming order of data chunks. [ABSTRACT FROM AUTHOR]

Most distributed computing applications require an effective scheduling algorithm to distribute and assign client’s tasks running on a set of processors. The existing algorithms assumed that the scheduled tasks can be simultaneously and ideally sent and received from the processors without any latent delay. However, this assumption is obviously impractical and unrealizable due to lack of consideration for constraints caused by the limited number of existing I/O ports at the client site. Such an I/O port constraint confines the overall throughputs of the computing systems. This study investigates the theoretical scheduling patterns under the constraint of one I/O port which typically exists to achieve optimal makespan and latent delay. A heuristic algorithm to effectively schedule the given set of tasks is also proposed. Two primary scheduling patterns leading to the optimal makespan and delay are discovered. Performance of the proposed scheduling is better than other scheduling algorithms under the imposed constraints in terms of shorter makespan and less latent delay, in particular, the average time complexity is equal to O ( n 2 ). [ABSTRACT FROM AUTHOR]

A new aspect of imbalanced data classification was studied. Unlike the classical imbalanced data classification where the cause of problem is due to the difference of data sizes, our study concerns only the situation when there exists an overlap between two classes. When one class overlaps another class, there are three regions induced from the overlap. The first region is the overlapped region between two classes. The rest is the non-overlapped region of each class. The imbalance situation is obviously caused by the different amount of data at the overlapped region and non-overlapped region. In this situation, the difference of data sizes from different classes is not the main concern and has no effect on the accuracy of classification. In this research, a combined technique, called Soft-Hybrid algorithm, was proposed for improving classification performance. The technique was divided into two main phases: boundary region determination and responsive classification algorithms for each sub-area. In the first phase, data were grouped as (1) non-overlapping data, (2) borderline data, and (3) overlapping data. Learning data using modified Hausdorff Distance, Radial Basis Function Network and K-Means clustering technique with Mahalanobis Distance. Then, modified Kernel Learning Method, modified DBSCAN and RBF network were applied to classify the data into proper groups based on statistical values from the classification phase. Finally, the results of all techniques were combined. The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data having large overlapping sections based on TP rate, F-measure and G-mean measures. Moreover, the computational times of the proposed method were lower than the standard algorithms used for this type of this problem. [ABSTRACT FROM AUTHOR]