This paper proposes a robust anti-tamper protection scheme to protect any critical regions of a program from being modified, using possibly a large number of lightweight protection units, called protectors, installed among the program code. A protector would cause an incorrect execution if the code protected by it has been tempered. The protectors are organized in the form of a protection tree. The root node is a critical region, and other nodes are protectors. The protection scheme also supports non-deterministic execution of functions. Modifying any critical region in the protected program has been shown to require an exponential time. Experiment results show that the proposed scheme would not increase noticeably the program execution time.

Abstract There is recently an increasing interest in applications based on localization of mobile objects in outdoor environment. Many existing localization solutions rely primarily on a hybrid wireless network/dead reckoning (DR) scheme, as the regular wireless network can hardly estimate a position with satisfactory accuracy in bad channel conditions. However, the DR can involve considerable hardware investments and operating costs, moreover, it suffers from serious error accumulations in motion measurements of the object. To remedy drawbacks of the hybrid scheme, we present a mobility enhanced localization (MEL), in which the mobile object serves as a pseudo beacon. The proposed scheme enables network localization in those areas where traditional wireless networks may not work, and thus, avoids large investments the special hardware cost and error accumulations of DR. We further propose a weighted preprocessing method for classical localization algorithms, since theoretical analysis shows that they fail to consider some boundary conditions. Extensive real world Global Positioning System (GPS) experimental results demonstrate the superiority of the proposed MEL scheme. Copyright © 2008 John Wiley & Sons, Ltd.

One of the important issues in wireless localization is concerned with the identification of non-line-of-sight (NLOS) radio signal propagation, since NLOS propagation is considered the dominant source of localization error. The existing identification methods focus on "consistency" tests, based on the assumption that localization involving the use of NLOS radio signals cannot be performed in a consistent manner. However, the validity of the foregoing assumption has not been properly investigated. This paper presents a theoretical analysis of the localization using NLOS radio signals, and shows that the assumption would no longer be valid when the mobile user is located outside the convex hull of the underlying beacons. As a result, existing NLOS identification methods, as well as many localization approaches, could perform unsatisfactorily. The importance of the proposed convex hull condition for NLOS identification is confirmed by extensive simulation results.

Shape is a fundamental image feature used in content-based image-retrieval systems. This paper proposes a robust and effective shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments (ZMs). As the rotation of an image has an impact on the ZM phase coefficients of the image, existing proposals normally use magnitude-only ZM as the image feature. In this paper, we compare, by using a mathematical form of analysis, the amount of visual information captured by ZM phase and the amount captured by ZM magnitude. This analysis shows that the ZM phase captures significant information for image reconstruction. We therefore propose combining both the magnitude and phase coefficients to form a new shape descriptor, referred to as invariant ZM descriptor (IZMD). The scale and translation invariance of IZMD could be obtained by prenormalizing the image using the geometrical moments. To make the phase invariant to rotation, we perform a phase correction while extracting the IZMD features. Experiment results show that the proposed shape feature is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. The proposed IZMD feature also outperforms the commonly used magnitude-only ZMD in terms of noise robustness and object discriminability.

Due to the advent of many location based applications, mobile localization in outdoor environments has recently attracted much attention. Many existing localization solutions rely primarily on a hybrid wireless network/dead reckoning (DR) method, as the regular wireless network can hardly estimate a location with satisfactory accuracy in bad channel conditions. However, the DR scheme can involve considerable hardware investments, moreover, it suffers from serious error accumulations in motion measurements of a mobile object. To remedy drawbacks of the hybrid method, we present a novel mobile localization scheme based on the concept of pseudo beacon. The proposed scheme can perform network localization in those areas where traditional wireless networks may not work, and thus, avoids large investments the special hardware cost and error accumulations of DR. Extensive real world GPS experimental results demonstrate the significant superiority of the proposed scheme.

As human attention is an effective mechanism for information prioritizing and selecting, it provides a practical approach for intelligent shot similarity matching. In this paper, we propose an attention-driven video interpretation method using an efficient spatiotemporal attention detection framework. The motion attention detection in most existing methods is unstable and computationally expensive. Avoiding calculating motion explicitly, the proposed framework generates motion saliency using the rank deficiency of grayscale gradient tensors. To address an ill-posed weight determination problem, an adaptive fusion method is proposed for motion and spatial saliency integration by highlighting the more reliable saliency maps. An attention-drive matching strategy is proposed by converting attention values to importance factors, which subsequently boost the attended regions in region-based shot matching. A global feature-based matching strategy is also included the attention-driven strategy, to address cases where visual attention detection is less applicable. Experiment results demonstrate the advantages of the proposed method in similarity matching.

Automatic detection of wipes and their frame ranges is important for the purpose of reliable video parsing and video database indexing. Wipes are difficult to detect because of the complexity and variety of the transition effects. Many of the existing wipe detection algorithms could detect only a few wipe effects. The false/miss detection problem caused by motion is also very serious. In this paper, we propose a novel wipe detection algorithm that can detect most wipe effects with accurate frame ranges. We carefully model a wipe based on its nature and then use the model to filter out possible confusion caused by motion or other transition effects. More precisely, properties of independence and completeness are proposed to characterize an ideal wipe; frame ranges of potential wipes are located by finding sequences which are a close approximation to an ideal wipe. Bayes rule is applied to each potential wipe to statistically estimate an adaptive threshold for the purpose of wipe verification. Experiment results on videos with different genres show that the proposed methodology can be used to detect various wipe effects effectively

Within the framework of spatiotemporal attention detection, this paper proposes an efficient method for focus of attention (FOA) detection which involves combining adaptively the spatial and motion attention to form an overall attention map. Without computing motion explicitly, it detects motion attention using the rank deficiency of grayscale gradient tensors. We also propose an attention-driven shot matching method using primarily FOA. Experimental results demonstrate the effectiveness of the proposed FOA detection method and the proposed shot matching approach outperforms other published methods.

This paper proposes an effective method for converting any fast DCT algorithm into an approximate multiplierless version. Basically it approximates any constant in the original transform by a signed digit representation. We developed an efficient algorithm to convert any constant into a signed digit string with a minimum number of non-zero signed digits and a reduced length. As the accuracy of an approximated algorithm depends critically on the assignment of signed digits to the constants, this paper formulated an effective algorithm for finding an effective signed digits configuration which could minimize the MSE of an approximated DCT algorithm with a specified complexity. Experiment results show that the AAN's fast DCT algorithm, approximated by the proposed method and using an optimized configuration can be used to reconstruct images with high visual quality in terms of PSNR

In this paper an effective method of using SVM classifier for multiple feature classification is proposed. Compared with traditional combination methods where all needed base classifiers should be trained before the decision combination, the proposed approach is to train individual classifiers and combine the decisions of these base classifiers at the same time. Thus the complexity of the training can be reduced because our proposed method involves solving only one optimization problem while several optimization problems should be solved for traditional methods. Furthermore, during the combination, our proposed approach takes into account both a base classifier's performance on the training data and its generalization ability while traditional combination approaches consider only a base classifier's performance on the training data. The experiments proved the efficiency of our proposed approach

Abstract The success of content‐based image retrieval (CBIR) relies critically on the ability to find effective image features to represent the database images. The shape of an object is a fundamental image feature and belongs to one of the most important image features used in CBIR. In this article we propose a robust and effective shape feature known as the compound image descriptor (CID), which combines the Fourier transform (FT) magnitude and phase coefficients with the global features. The underlying FT coefficients have been shown analytically to be invariant to rotation, translation, and scaling. We also present details of the underlying innovative shape feature extraction method. The global features, besides being incorporated with the FT coefficients to form the CID, are also used to filter out the highly dissimilar images during the image retrieval process. Thus, they serve a dual purpose of improving the accuracy and hence the robustness of the shape descriptor, and of speeding up the retrieval process, leading to a reduced query response time. Experiment results show that the proposed shape descriptor is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. It also outperforms other recently published proposals, such as the generic Fourier descriptor (Zhang & Lu, 2002).

We address the problem of efficiency in texture analysis for synthetic aperture radar (SAR) imagery. Motivated by the statistical occupancy model, we introduce the notion of patch reoccurrences. Using the reoccurrences, we propose the use of approximate textural features in analysis of SAR images. We describe how the proposed approximate features can be extracted for two popular texture analysis methods-the gray-level cooccurrence matrix and Gabor wavelets. Results on image texture classification show that the proposed method can provide an improved efficiency in the analysis of SAR imagery, without introducing any significant degradation in the classification results.

An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods.

An adaptive mechanism for video partitioning in the transform domain is proposed. Different quantitative measures for motion complexity and activity levels in a scene are defined, based on which a video scene can be consistently categorised into identifiable classes. Further, the video quality, as measured by the mean square error (MSE) is related to certain parameters used in video partitioning. Adaptability is realized by tailoring the parameters of the video partitioning algorithm to the specific characteristics of the video scene, as embodied in the video scene class and the video quality. Experimental results are included.

Reflector attack [Vern Paxson (2001)] belongs to one of the most serious types of denial-of-service (DoS) attacks, which can hardly be traced by contemporary traceback techniques, since the marked information written by any routers between the attacker and the reflectors will be lost in the replied packets from the reflectors. We propose a reflective algebraic marking scheme for tracing DoS and DDoS attacks, as well as reflector attacks. The proposed marking scheme contains three algorithms, namely the marking, reflection and reconstruction algorithms, which have been well tested through extensive simulation experiments. The results show that the marking scheme can achieve a high performance in tracing the sources of the potential attack packets. In addition, it produces negligible false positives; whereas other current methods usually produce a certain amount of false positives.

Abstract This article introduces an effective rotation and scale invariant log‐polar wavelet texture feature for image retrieval. The proposed feature is an attempt to enhance the existing content‐based image retrieval systems that largely present difficulty in coping with images with changes in orientations and scales. The underlying feature extraction process involves a log‐polar transform followed by an adaptive row shift invariant wavelet packet transform. The log‐polar transform converts a given image into a rotation and scale invariant but row‐shifted image, which is then further processed through an adaptive row‐shift invariant wavelet packet transform operation to generate adaptively selected subbands of rotation and scale invariant wavelet coefficients, based on an information cost function. An energy signature is computed for each subband of these wavelet coefficients. To reduce feature dimensionality, only the most dominant log‐polar wavelet energy signatures are selected for the feature vector for image retrieval. The overall feature extraction process is quite efficient and involves only O(n · log n) complexity. Experimental results show that this rotation and scale invariant wavelet feature is quite effective for image retrieval and outperforms the traditional wavelet packet signatures.

Classification of texture images is important in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n /spl middot/ log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.

An effective rotation invariant polar-wavelet texture feature for content based image retrieval is proposed. The feature extraction process involves a polar transform followed by an adaptive row shift invariant wavelet packet transform. The polar transform converts a given image into a rotation-invariant but row-shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation invariant wavelet coefficients with respect to an information cost function. An energy signature is computed for each sub-band of these wavelet coefficients. In order to reduce feature dimensionality, only the most dominant polar-wavelet energy signatures are selected as feature vectors for image retrieval. The whole feature extraction process is quite efficient and involves only O(n/spl middot/log n) complexity. Experimental results show that this rotation-invariant texture feature is effective and outperforms traditional wavelet packet signatures.

The color ratio approach to indexing has been found to be robust and effective in indexing image and video databases, in different color spaces, and when using transformed color features, such as those from the Karhunen-Loeve transform (KLT) or the discrete cosine transform (DCT). However, the reason for the superior performance of the color ratio model, especially on different color spaces or with transformed color features has, at best, been speculative. This paper develops a generalized form for the color ratio model, based on which we characterize the general distribution of the color ratios. From the distribution, we present a theory that explains and supports the performance of the color ratio approach in image and video indexing. It is shown that the same theory accounts for its effectiveness in different color spaces and in the transform domain. Some general problems encountered in using the original retinex lightness algorithm, and some other issues specific to ratio-based color indexing are discussed in the light of the theory. Results are presented which show that the proposed theory is supported by empirical evidence.

This paper proposes a novel approach to the classification of rotated texture images. The proposed classification method involves decomposing a texture image with a family of real orthonormal wavelet bases for different levels, computing the wavelet packet coefficients, and computing the energy signatures using the wavelet packet coefficients. Such energy signatures are sorted and used as a feature for texture image classification. We employ a Mahalanobis distance classifier to classify a set of twenty distinct natural textures selected from the Brodatz album. Experimental results, based on a large sample data set having different orientations, show that the proposed method outperforms other methods which may perform well in the classification of texture images having the same orientation.

The analysis of visual information often involves the manipulation of enormous volumes of data. If some tolerance is allowed in the results, orders of magnitude improvement in efficiency can be achieved in such analysis by appropriate selective processing, without necessarily considering all the data features. To guarantee that the error introduced does not exceed the allowed limit, a certain minimum proportion of the data must be involved in the analysis. This proportion cannot be determined arbitrarily. It should be chosen based on some formal methods, with a consideration of the error inherent in the data. This paper presents some techniques for improving the retrieval efficiency in image-based information systems, with performance guarantees on the reliability of results. Using the statistical theory of occupancy, it develops a model for the formal selection of the minimal subset of image features to be involved in histogram-based similarity evaluation. This guarantees that decisions based on the minimum proportion are always the same as (or close to) the one that would have been reached by considering all the features. Results on real and simulated data show the performance of the model on speedup, robustness, scalability, and performance guarantees.