Ö. Nezih Gerek

On feature extraction for spam e-mail detection

Electronic mail is an important communication method for most computer users. Spam e-mails however consume bandwidth resource, fill-up server storage and are also a waste of time to tackle.The general way to label an e-mail as spam or non-spam is to set up a finite set of discriminative features and use a classifier for the detection. In most cases, the selection of such features is empirically verified. In this paper, two different methods are proposed to select the most discriminative features among a set of reasonably arbitrary features for spam e-mail detection. The selection methods are developed using the Common Vector Approach (CVA) which is actually a subspace-based pattern classifier.Experimental results indicate that the proposed feature selection methods give considerable reduction on the number of features without affecting recognition rates.

A novel 2-D model approach for the prediction of hourly solar radiation

In this work, a two-dimensional (2-D) representation of the hourly solar radiation data is proposed. The model enables accurate forecasting using image prediction methods. One year solar radiation data that is acquired and collected between August 1, 2005 and July 30, 2006 in Iki Eylul campus of Anadolu University, and a 2-D representation is formed to construct an image data. The data is in raster scan form, so the rows and columns of the image matrix indicate days and hours, respectively. To test the forecasting efficiency of the model, first 1-D and 2-D optimal 3-tap linear filters are calculated and applied. Then, the forecasting is tested through three input one output feed-forward neural networks (NN). One year data is used for training, and 2 month(from August 1,2006 to September 30,2006) for testing. Optimal linear filters and NN models are compared in the sense of root mean square error (RMSE). It is observed that the 2-D model has advantages over the 1- D representation. Furthermore, the NN model accurately converges to forecasting errors smaller than the linear prediction filter results.

Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets

An offline feature selection and evaluation mechanism is used in order to develop a robust visual tracking scheme for sea-surface and aerial targets. The covariance descriptors, known to constitute an effi- cient signature set in object detection and classification problems, are used in the feature extraction phase of the proposed scheme. The per- formance of feature sets are compared using support vector machines, and those resulting in the highest detection performance are used in the covariance based tracker. The tracking performance is evaluated in dif- ferent scenarios using different performance measures with respect to ground truth target positions. The proposed tracking scheme is observed to track sea-surface and aerial targets with plausible accuracies, and the results show that gradient-based features, together with the pixel locations and intensity values, provide robust target tracking in both surveillance scenarios. The performance of the proposed tracking strategy is also compared with some well-known trackers including correlation, Kanade- Lucas-Tomasi feature, and scale invariant feature transform-based track- ers. Experimental results and observations show that the proposed target tracking scheme outperforms other trackers in both air and sea surveil- lance scenarios. C

Salient point region covariance descriptor for target tracking

Abstract. Features extracted at salient points are used to construct a region covariance descriptor (RCD) for target tracking. In the classical approach, the RCD is computed by using the features at each pixel location, which increases the computational cost in many cases. This approach is redundant because image statistics do not change significantly between neighboring image pixels. Furthermore, this redundancy may decrease tracking accuracy while tracking large targets because statistics of flat regions dominate region covariance matrix. In the proposed approach, salient points are extracted via the Shi and Tomasi’s minimum eigenvalue method over a Hessian matrix, and the RCD features extracted only at these salient points are used in target tracking. Experimental results indicate that the salient point RCD scheme provides comparable and even better tracking results compared to a classical RCD-based approach, scale-invariant feature transform, and speeded-up robust features-based trackers while providing a computationally more efficient structure.

Region covariance descriptors calculated over the salient points for target tracking

Features extracted at salient points in the image are used to construct region covariance descriptor (RCD) for target tracking purposes. In the classical approach, the RCD is computed by using the features at each pixel location and thus, increases the computational cost in the scenarios where large targets are tracked. The approach in which the features at each pixel location are used, is redundant in cases where image statistics do not change significantly between neighboring pixels. Furthermore, this may decrease the tracking accuracy while tracking large targets which have background dominating structures. In the proposed approach, the salient points are extracted via the Shi and Tomasis minimum eigenvalue method and a descriptor based target tracking structure is constructed based on the features extracted only at these salient points. Experimental results indicate that the proposed method provides comparable and in some cases even better tracking results compared to the classical method while providing a computationally more efficient structure.

Tracking of platforms above sea-level in visual band videos using covariance based tracker and feature selection

In this study, a feature based tracker is developed in order to track platforms above sea-level in visual band videos. The covariance descriptor, known as an efficient method in object detection and classification problems, is used in the feature extraction phase of the tracker. The features or feature sets, extracted from the images in the database that is constructed for this work, are compared by using Support Vector Machine and by using the features or feature sets providing the best target/background separation, in the covariance based tracker successful tracking of platforms above sea-level is provided. Experimental results show that by adding or extracting appropriate features to covariance based descriptors, the proposed method can track different platforms successfully.

A target detection algorithm based on peripheral anomaly

Automatic target detection algorithms enable in military and civilian applications the detection of low-contrast and spatially small targets that can not be detected by a human operator and remove the requirement of permanent operator control for surveillance systems. Considering many different background patterns and target models, it is not possible to define a single target detection algorithm that succeeds in all scenarios. In this paper, a novel target detection algorithm is proposed for targets above sea-level and sky targets. Peripheral anomaly values are calculated for image blocks depending on the difference of the statistical data between target and background patterns and target and background regions are differentiated by comparing these values with scenario dependent threshold values. Experimental results show that the proposed method has high success rate in the detection of targets above sea-level and sky targets in visual band images.

Power quantity event detection using joint 2D-wavelet subspaces

In this work, we present a novel 2D representation of power system for the automatic analysis of transients. The representation is composed of a matrix whose rows are formed by time segments of digital waveforms. By the appropriate selection of the time segment length, the 2D data exhibits wave-like image shapes. The general shape is immediately disturbed whenever a quality event occurs. We propose the use of two-dimensional discrete wavelet transforms (2D-DWT) to detect these shape disturbances. It has been observed that, after omitting the approximation space signals of the wavelet transform and denoising the detail space signals, the inverse 2D-DWT provides good detection and localization results, even for cases where conventional methods fail. Examples are presented.In this work, we present a novel 2D representation of power system for the automatic analysis of transients. The representation is composed of a matrix whose rows are formed by time segments of digital waveforms. By the appropriate selection of the time segment length, the 2D data exhibits wave-like image shapes. The general shape is immediately disturbed whenever a quality event occurs. We propose the use of two-dimensional discrete wavelet transforms (2D-DWT) to detect these shape disturbances. It has been observed that, after omitting the approximation space signals of the wavelet transform and denoising the detail space signals, the inverse 2D-DWT provides good detection and localization results, even for cases where conventional methods fail. Examples are presented.