Description:
Abstract— Image sequences of smoke, fire, etc. are known as dynamic textures. Research is mostly limited to characterization of single dynamic textures. In this paper we address the problem of detecting the presence of multiple dynamic textures in an image sequence by establishing a correspondence between the feature space of dynamic textures and that of their mixture in an image sequence. Accuracy of our proposed technique is both analytically and empirically established with detection experiments yielding 92.5% average accuracy on a diverse set of dynamic texture mixtures in synthetically generated as well as real-world image sequences.
Description:
Because of the high dimensionality of the microarray data sets, feature selection (FS) has become an important challenge in molecular classification. Using the degree of differential prioritization (DDP) between relevance and antiredundancy, our proposed DDP-based FS technique is capable of achieving better accuracies than those previously reported, using a smaller predictor set. However, previously, we have neither devised nor used any method for determining the value of the DDP to be used for the data set of interest before the FS process. In this article, we propose a system for predicting the optimal value of the DDP, which costs less computationally than conventional tuning while maintaining the independence of the FS technique from the type of underlying classifier used
Description:
There are many applications, such as image copyright protection, where transformed images of a given test image need to be identified. The solution to this identification problem consists of two main stages. In stage one, certain representative features, such as corners, are detected in all images. In stage two, the representative features of the test image and the stored images are compared to identify the transformed images for the test image. Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. However, the arc-length used to parameterize the planar curves by the existing CSS detectors is not invariant to geometric transformations such as scaling. As a solution to stage one, this paper presents an improved CSS corner detector using the affine-length parameterization which is relatively invariant to affine transformations. We then present an improved corner matching technique as a solution to the stage two. Finally, we apply the proposed corner detection and matching techniques to identify the transformed images for a given image and report the promising results.
Description:
This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework.
Description:
Complex regulatory networks often have to be further expanded and improved with regard to the unknown effects of additional parameters and factors that can emit a disturbing influence on the key variables under consideration. The concept of target-environment (TE) networks provides a holistic framework for the analysis of such parameter-dependent multi-modal systems. In this study, we consider time-discrete TE regulatory systems with spline entries. We introduce a new regression model for these particular two-modal systems that allows us to determine the unknown system parameters by applying the multivariate adaptive regression spline (MARS) technique and the newly developed conic multivariate adaptive regression spline (CMARS) method. We obtain a relaxation by means of continuous optimization, especially, conic quadratic programming (CQP) that could be conducted by interior point methods. Finally, a numerical example demonstrates the efficiency of the spline-based approach.
Description:
Secondary service providers can buy spectrum resources from primary service providers for a short or long period of time and exploit it to solve the problem of spectrum scarcity. This buying decision of spectrum buyers can depend on several factors including pricing of the spectrum, reputation of a seller, and duration of the contract and spectrum quality. However, existing pricing models for dynamic spectrum access consider mainly bandwidth which makes them unsuitable for real-world trading. In this paper, we consider these issues related to the pricing of spectrum sale in terms of microeconomic theories. First, we consider reputation of spectrum sellers and update it dynamically by considering a buyer's own trading experience with the sellers and collecting recommendations on sellers from other buyers. Second, trustworthiness of recommenders as well as incentive to encourage recommendations are modeled. Third, contract duration and spectrum quality are incorporated such that a buyer's utility is formulated as a function of buyer's resource requirement, reputation of seller and trustworthiness of recommenders. Fourth, the model is analyzed using dynamic pricing of the market and the solution is obtained using market equilibrium. Results demonstrate the superiority of our model over the existing microeconomic models for dynamic spectrum trading.
Description:
Despite their wide spread use, nearest neighbour density estimators have two fundamental limitations: O(n2) time complexity and O(n) space complexity. Both limitations constrain nearest neighbour density estimators to small data sets only. Recent progress using indexing schemes has improved to near linear time complexity only.We propose a new approach, called LiNearN for Linear time Nearest Neighbour algorithm, that yields the first nearest neighbour density estimator having O(n) time complexity and constant space complexity, as far as we know. This is achieved without using any indexing scheme because LiNearN uses a subsampling approach for which the subsample values are significantly less than the data size. Like existing density estimators, our asymptotic analysis reveals that the new density estimator has a parameter to trade off between bias and variance. We show that algorithms based on the new nearest neighbour density estimator can easily scale up to data sets with millions of instances in anomaly detection and clustering tasks. Highlights•Reject the premise that a NN algorithm must find the NN for every instance.•The first NN density estimator that has O(n) time complexity and O(1) space complexity.•These complexities are achieved without using any indexing scheme.•Our asymptotic analysis reveals that it trades off between bias and variance.•Easily scales up to large data sets in anomaly detection and clustering tasks.
Description:
Vertical handoff in next generation heterogeneous wireless networks is a multi-dimensional issue. In this article we propose a multi-dimensional adaptive SINR based vertical handoff algorithm (MASVH) which uses the combined effects of SINR, user required bandwidth, user traffic cost and utilization from participating access networks to make handoff decisions for multi-attribute QoS consideration. Simulation results confirm that the new MASVH algorithm improves the system performance in terms of higher throughput and lower dropping probability, as well as reduces the user traffic cost for accessing the integrated wireless networks.
Description:
Early stage faults detection for machine health monitoring demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR). Vibration signal which is used for signature matching in case of abnormality detection and diagnosis, requires robust tools such as wavelet transform (WT) for time-frequency analysis. WT is specifically used to deal with nonstationary signals. In order to guarantee improved performance under poor SNR, this paper proposes a scheme for feature extraction based on fourth-order cumulant and stationary wavelet transform (FoCSWT). Higher order cumulants have the tendency to mitigate the impact of Gaussian noise. Fourth-order cumulant corresponds to the "peakedness" of the random distribution and the fault detection capability quantifies it as the most dominant cumulant among higher order statistics. Stationary wavelet transform is used to avoid down-sampling on the vibration data prior to feature extraction which gives better estimation of statistical parameters of the data distribution and gives performance enhancement in terms of fault classification accuracy. Simulation studies show that FoCSWT outperforms the existing techniques in terms of fault detection accuracies under poor SNR.
Description:
The purpose of this paper is to propose a modified version of Failure Mode and Effects Analysis (FMEA) to alleviate its drawbacks. FMEA is an important tool in risk evaluation and finding the priority of potential failure modes for corrective actions. In the proposed method, the Universal Generating Function (UGF) approach has been used to improve the assessment capability of the conventional Risk Priority Number (RPN) in ranking. The new method is named as URPN. It generates the most number of unique values in comparison with the previous methods and considers relative importance for the parameters while it is easy to compute. More unique numbers help to avoid from having the same priority level for different failure modes which represent various risk levels. A case study has been employed to demonstrate that the URPN not only can improve the shortcomings but also is able to provide accurate values for risk assessment. Copyright (c) 2015 John Wiley & Sons, Ltd.
Description:
Abstract— Corner detectors have many applications in computer vision and image identification and retrieval. Contour-based corner detectors directly or indirectly estimate a significance measure (e.g., curvature) on the points of a planar curve, and select the curvature extrema points as corners. While an extensive number of contour-based corner detectors have been proposed over the last four decades, there is no comparative study of recently proposed detectors. This paper is an attempt to fill this gap. The general framework of contour-based corner detection is presented, and two major issues – curve smoothing and curvature estimation, which have major impacts on the corner detection performance, are discussed. A number of promising detectors are compared using both automatic and manual evaluation systems on two large datasets. It is observed that while the detectors using indirect curvature estimation techniques are more robust, the detectors using direct curvature estimation techniques are faster.
Description:
Bezier curves (BC) have become fundamental tools in many challenging and varied applications, ranging from computer-aided geometric design to generic object shape descriptors. A major limitation of the classical Bezier curve, however, is that only global information about its control points (CP) is considered, so there can often be a large gap between the curve and its control polygon, leading to large distortion in shape representation. While strategies such as degree elevation, composite BC, refinement and subdivision reduce this gap, they also increase the number of CP and hence bit-rate, and computational complexity. This paper presents novel contributions to BC theory, with the introduction of quasi-Bezier curves (QBC), which seamlessly integrate localised CP information into the inherent global Bezier framework, with no increase in either the number of CP or order of computational complexity. QBC crucially retains the core properties of the classical BC, such as geometric continuity and affine invariance, and can be embedded into the vertex-based shape coding and shape descriptor framework to enhance rate-distortion performance. The performance of QBC has been empirically tested upon a number of natural and synthetically shaped objects, with both qualitative and quantitative results confirming its consistently superior approximation performance in comparison with both the classical BC and other established BC-based shape descriptor methods.