Naif Alajlan

Shape retrieval using triangle-area representation and dynamic space warping

In this paper, we present a shape retrieval method using triangle-area representation for nonrigid shapes with closed contours. The representation utilizes the areas of the triangles formed by the boundary points to measure the convexity/concavity of each point at different scales (or triangle side lengths). This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, and scaling, and robust against noise and moderate amounts of occlusion. In the matching stage, a dynamic space warping (DSW) algorithm is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Then, a distance is derived based on the optimal correspondence. The performance of our method is demonstrated using four standard tests on two well-known shape databases. The results show the superiority of our method over other recent methods in the literature.

Geometry-Based Image Retrieval in Binary Image Databases

In this paper, a geometry-based image retrieval system is developed for multiobject images. We model both shape and topology of image objects using a structured representation called curvature tree (CT). The hierarchy of the CT reflects the inclusion relationships between the image objects. To facilitate shape-based matching, triangle-area representation (TAR) of each object is stored at the corresponding node in the CT. The similarity between two multiobject images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adopt a recursive algorithm to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multiobject images. Experiments on a database of 13,500 real and synthesized medical images and the MPEG-7 CE-1 database of 1,400 shape images have shown the effectiveness of the proposed method.

Deep learning approach for active classification of electrocardiogram signals

In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods.

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

Recently, a new machine learning approach that is termed as the extreme learning machine (ELM) has been introduced in the literature. This approach is characterized by a unified formulation for regression, binary, and multiclass classification problems, and the related solution is given in an analytical compact form. In this letter, we propose an efficient classification method for hyperspectral images based on this machine learning approach. To address the model selection issue that is associated with the ELM, we develop an automatic-solution-based differential evolution (DE). This simple yet powerful evolutionary optimization algorithm uses cross-validation accuracy as a performance indicator for determining the optimal ELM parameters. Experimental results obtained from four benchmark hyperspectral data sets confirm the attractive properties of the proposed DE-ELM method in terms of classification accuracy and computation time.

A wavelet optimization approach for ECG signal classification

a b s t r a c t Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accu- racy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimina- tion capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experi- mental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accu- racy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet

Fusion of supervised and unsupervised learning for improved classification of hyperspectral images

In this paper, we introduce a novel framework for improved classification of hyperspectral images based on the combination of supervised and unsupervised learning paradigms. In particular, we propose to fuse the capabilities of the support vector machine classifier and the fuzzy C-means clustering algorithm. While the former is used to generate a spectral-based classification map, the latter is adopted to provide an ensemble of clustering maps. To reduce the computation complexity, the most representative spectral channels identified by the Markov Fisher Selector algorithm are used during the clustering process. Then, these maps are successively labeled via a pairwise relabeling procedure with respect to the pixel-based classification map using voting rules. To generate the final classification result, we propose to aggregate the obtained set of spectro-spatial maps through different fusion methods based on voting rules and Markov Random Field theory. Experimental results obtained on two hyperspectral images acquired by the reflective optics system imaging spectrometer and the airborne visible/infrared imaging spectrometer, respectively; confirm the promising capabilities of the proposed framework.

Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images

In this letter, we propose an efficient multiclass active learning (AL) method for remote sensing image classification. We fuse the capabilities of an extreme learning machine (ELM) classifier and graph-based optimization methods to boost the classification accuracy while minimizing the user interaction. First, we use the ELM to generate an initial label estimation of the unlabeled image pixels. Then, we optimize a graph-based functional energy that integrates the ELM outputs as an initial estimation of the image structure. As for the ELM, the solution to this multiclass optimization problem leads to a system of linear equations. Due to the sparse Laplacian matrix built from the lattice graph defined on the image pixels, the optimization problem is solved in a linear time. In the experiments, we report and discuss the results of the proposed AL method on two very high resolution images acquired by IKONOS-2 and GoeEye-1, as well as the well-known Pavia University hyperspectral image.

Decision Making with Ordinal Payoffs Under Dempster–Shafer Type Uncertainty

Our focus is on decision making in uncertain environments. We first introduce the Dempster–Shafer framework to model the uncertainty associated with possible outcomes. We then describe an approach for decision making when our uncertainty is captured using the Dempster–Shafer model and where the payoffs are numeric values. An important part of this approach is the role of the decision attitude as well as the aggregation of the possible payoffs. We then look at the situation where the payoffs, rather than being numbers, are values drawn from an ordinal scale. This requires us to provide appropriate operations for combining payoffs drawn from an ordinal scale.

Land-Use Classification With Compressive Sensing Multifeature Fusion

In this letter, we formulate a land-use (LU) classification problem within a compressive sensing (CS) fusion framework. CS aims at providing a compact representation form after a given query image has been processed with an opportune feature extraction type. In particular, residuals are generated from the image reconstruction with dictionaries associated with the available set of possible LUs and gathered to form a single-feature image pattern. The patterns obtained from different types of features are then fused to provide the final LU estimate. Two simple fusion strategies are adopted for such purpose. As demonstrated by experiments ran on the basis of a public benchmark database, the proposed method can achieve substantial classification accuracy gains over reference methods.

A generalized framework for mean aggregation: Toward the modeling of cognitive aspects

We provide an overview of mean/averaging operators. We introduce the basic OWA operator and look at some cases of the generalized OWA operator. We next look at the issue of importance weighted mean aggregation. We provide a generalized formulation using a fuzzy measure to convey information about the importances of the different arguments in the aggregation. We look at some different measures and the associated importance formulation they manifest. We further generalize our formulation by allowing for the inclusion of an attitudinal aggregation function. This allows us to implement many different types of aggregation including Max, Min and Median. Finally we provide a simple parameterized formulation for generalized class of mean operators.