252674
Recognizing picture-taking environment from satellite images: A feasibility study
Semantic event recognition based only on vision cues has had limited success on unconstrained still pictures. Metadata related to picture taking provides contextual cues independent of the image content and can be used to improve classification performance. With geotagged pictures, we investigate the novel use of satellite images (e.g., supplied by Google Earth¿) corresponding to the GPS (global positioning system) coordinates to recognize a picture-taking environment. Initial assessment reveals that with minimum training humans can achieve high accuracy in the recognition of terrain environment based on views from above. We propose to employ both color- and structure-based vocabularies for characterizing satellite images in terms of 14 of the most interesting classes, including residential areas, commercial areas, sports venues, parks, and schools. A multiclass AdaBoost engine is trained to predict terrain environment given a satellite aerial image of the location. Initial experimental results using two-fold cross-validation have shown promise for employing the proposed scheme for event recognition.
17673349
Event recognition: viewing the world with a third eye
Semantic event recognition based only on vision cues is a challenging problem. This problem is particularly acute when the application domain is unconstrained still images available on the Internet or in personal repositories. In recent years, it has been shown that metadata captured with pictures can provide valuable contextual cues complementary to the image content and can be used to improve classification performance. With the recent geotagging phenomenon, an important piece of metadata available with many geotagged pictures now on the World Wide Web is GPS information. In this study, we obtain satellite images corresponding to picture location data and investigate their novel use to recognize the picture-taking environment, as if through a third eye above the object. Additionally, we combine this inference with classical vision-based event detection methods and study the synergistic fusion of the two approaches. We employ both color- and structure-based visual vocabularies for characterizing ground and satellite images, respectively. Training of satellite image classifiers is done using a multiclass AdaBoost engine while the ground image classifiers are trained using SVMs. Modeling and prediction involve some of the most interesting semantic event-activity classes encountered in consumer pictures, including those that occur in residential areas, commercial areas, beaches, sports venues, and parks. The powerful fusion of the complementary views achieves significant performance improvement over the ground view baseline. With integrated GPS-capable cameras on the horizon, we believe that our line of research can revolutionize event recognition and media annotation in years to come.
5934719
A New Study on Distance Metrics as Similarity Measurement
Distance metric is widely used in similarity estimation. In this paper we find that the most popular Euclidean and Manhattan distance may not be suitable for all data distributions. A general guideline to establish the relation between a distribution model and its corresponding similarity estimation is proposed. Based on maximum likelihood theory, we propose new distance metrics, such as harmonic distance and geometric distance. Because the feature elements may be from heterogeneous sources and usually have different influence on similarity estimation, it is inappropriate to model the distribution as isotropic. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods
2279134
Toward Robust Distance Metric Analysis for Similarity Estimation
In this paper, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as harmonic distance and geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate feature model than the conventional Euclidean distance (SSD) and Manhattan distance (SAD). Because the feature elements are from heterogeneous sources and may have different influence on similarity estimation, the assumption of single isotropic distribution model is often inappropriate. We propose a novel boosted distance metric that not only finds the best distance metric that fits the distribution of the underlying elements but also selects the most important feature elements with respect to similarity. We experiment with different distance metrics for similarity estimation and compute the accuracy of different methods in two applications: stereo matching and motion tracking in video sequences. The boosted distance metric is tested on fifteen benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.
3723005
Self-supervised learning based on discriminative nonlinear features for image classification
For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach.
3025146
Robust Error Metric Analysis for Noise Estimation in Image Indexing
In many computer vision algorithms, the well known Euclidean or SSD (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise is Gaussian. However, Gaussian noise distribution assumption is often invalid. Previous research has found that other metrics such as double exponential metric or Cauchy metric provide better results, in accordance with the maximum likelihood approach. In this paper, we examine different error metrics and provide a general guideline to derive a rich set of nonlinear estimations. Our results on image databases show more robust results are obtained for noise estimation based on the proposed error metric analysis.
18970831
A Scalable, Vulnerability Modeling and Correlating Method for Network Security
Nowadays attacks are becoming increasingly frequent and sophisticated, and they are also becoming increasingly interconnected. Recent works in network security have demostrated the fact that combinations of vulnerability exploits are the typical means by which an attacker can break into a network. It is therefore in great need of performing vulnerability analysis to do security analysis first and take the initiative to find hidden safety problems, then plan effective security measures. In this paper, we propose an analysis model, which derives vulnerability analysis functionality from the interaction of three distinct processes: scanning, modeling and correlating. Scanning is served as a significant issue for identifying vulnerabilities. Modeling provides a concise representation for expressing fact base such as host configuration, vulnerability information, and network topology. Moreover, correlating is used to provide a perspective into correlating isolated vulnerabilities in order to construct layered attack graph. Transition rule is presented in scalable design, which enables highly efficient methods of vulnerability correlation algorithm. Finally, a real case study has been described to demonstrate the capability of our model.
10879292
Rate-limited EAFRP-a new improved model for high-speed network traffic
The Extended Alternating Fractal Renewal Process (EAFRP) model has recently been proposed for modeling the self-similar and impulsive traffic of high-speed networks. For mathematical simplicity, it assumes that the available transmission bandwidth in the network is infinite. In reality, the network has a limit R on the total traffic rate through it, and in addition, the ith user's traffic rate is often limited to special value L/sub i/, which is assigned by the bandwidth sharing protocol. We propose a model for single-user traffic, which, by taking into account the aforementioned rate limit L/sub i/ and R, and in the absence of congestion, provides insight on the distinctive two slope behavior of the loglog survival function of multiuser traffic. For small to medium number of users, such as in local area networks (LANs), the model results in non-Gaussian traffic, whereas as the number of users increases, the resulting traffic is Gaussian, both of which are consistent with real network measurements. We discuss model parameter estimation, provide queuing analysis of the multiple-user traffic model, and, based on real data, show that it achieves a closer approximation of the observed reality than existing models.
13918438
Multi-cue Based Visual Tracking in Clutter Scenes with Occlusions
Object tracking is important for video analysis applications. However, tracking through occlusions is a difficult task due to significant appearance changes of the objects. Approaches based on either global features or one kind of local features can not solve the problem completely. In this paper, a multi-cue based tracking approach is introduced. It combines a corner tracking with a color and a shape model to resolve the object tracking problem through occlusions for most scenes (indoor and outdoor).To obtain an objective evaluation of the proposed method, a set of detection and tracking measures are used to perform a quantitative analysis based on a large sequence dataset with ground-truth annotation. The experimental results show that the proposed approach works robustly under varying conditions.
1697432
A case for an interleaving constrained shared-memory multi-processor
Shared-memory multi-threaded programming is inherently more difficult than single-threaded programming. The main source of complexity is that, the threads of an application can interleave in so many different ways. To ensure correctness, a programmer has to test all possible thread interleavings, which, however, is impractical.
 Many rare thread interleavings remain untested in production systems, and they are the root cause for a majority of concurrency bugs. We propose a shared-memory multi-processor design that avoids untested interleavings to improve the correctness of a multi-threaded program. Since untested interleavings tend to occur infrequently at runtime, the performance cost of avoiding them is not high.
 We propose to encode the set of tested correct interleavings in a program's binary executable using Predecessor Set (PSet) constraints. These constraints are efficiently enforced at runtime using processor support, which ensures that the runtime follows a tested interleaving. We analyze several bugs in open source applications such as MySQL, Apache, Mozilla, etc., and show that, by enforcing PSet constraints, we can avoid not only data races and atomicity violations, but also other forms of concurrency bugs.
1807096
The effects of web site aesthetics and shopping task on consumer online purchasing behavior
This research-in-progress investigates the effects of website aesthetics and shopping task type on consumer attitude towards electronic shopping sites. The conceptualization of website aesthetics is based on the two-dimensional structure, namely, classical and expressive aesthetics, proposed by Lavie and Tractinsky (2004). The online shopping tasks are categorized as hedonic versus utilitarian shopping tasks. A pre-test was conducted for selection of utilitarian and hedonic products. Based on the results of the pre-test, a laboratory experiment was designed to capture the effects of website aesthetics on online consumer purchasing behavior across different shopping tasks.