11013164
Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification
In this paper, a novel hybrid dimension reduction technique for classification is proposed based on the hybrid analysis of principal component analysis (PCA) and linear discriminant analysis (LDA). LDA is known for capturing the most discriminant features of the data in the projected space while PCA is known for preserving the most descriptive ones after projection. Our hybrid technique integrates discriminant and descriptive information and finds a richer set of alternatives beyond LDA and PCA in a 2-D parametric space, which fits a specific classification task and data distribution better. Theoretical study shows that our technique also alleviates the singularity problem of scatter matrix, which is caused by small training set, and increases the effective dimension of the projected subspace. In order to find the hybrid features adaptively and avoid exhaustive parameter searching, we further propose a boosted hybrid analysis method that incorporates a nonlinear boosting process to enhance a set of hybrid classifiers and combine them into a more accurate one. Compared with the other techniques that aim at combining PCA and LDA, our approaches are novel because our method finds alternatives to LDA and PCA in a 2-D parameter space and the boosting process provides enhancement and robust combination of the classifiers. Extensive experiments are conducted on benchmark and real image databases to compare our proposed methods with the state-of-the-art linear and nonlinear discriminant analysis techniques. The results show the superior performance of our hybrid analysis methods
11912264
Toward Intelligent Use of Semantic Information on Subspace Discovery for Image Retrieval
Image retrieval has been widely used in many fields of science and engineering. The semantic concept of user interest is obtained by a learning process. Traditional techniques often assume the images are from certain distribution and all images from the same class are visually similar. Our study shows that those assumptions are inappropriate in many cases. To solve this problem we model the images as lying on non-linear subspaces embedded in the high dimensional space. We also find that a set of low-level feature subspaces may correspond to one high-level semantic concept. Unlike most unsupervised subspace learning techniques, we propose to intelligently use the semantic similarity and dissimilarity information provided by user in discovering the discriminant structure of image subspaces in respect to classification. Theoretical study shows that our methods converge to linear discriminant analysis if certain criteria are met. Extensive experiments are designed to evaluate the performance of our method and compare it to other state-of-the-art techniques. The results show the superior performance of our proposed method
45747
A new analysis of the value of unlabeled data in semi-supervised learning for image retrieval
There has been an increasing interest in using unlabeled data in semi-supervised learning for various classification problems. Previous work shows that unlabeled data can improve or degrade the classification performance depending on whether the model assumption matches the ground-truth data distribution, and also on the complexity of the classifier compared with the size of the labeled training set. In this paper, we provide a new analysis on the value of unlabeled data by considering different distributions of the labeled and unlabeled data and showing the migrating effect for semi-supervised learning. Extensive experiments have been performed in the context of image retrieval applications. Our approach evaluates the value of unlabeled data from a new aspect and is aimed to provide a guideline on how unlabeled data should be used
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
1550342
Toward an improved error metric
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 theoretical approach 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.
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.
10430483
Knowledge contribution in problem solving virtual communities: the mediating role of individual motivations
Increasing knowledge contribution in problem solving virtual communities (PSVCs) is a long-pursued question in the knowledge management arena for both researchers and practitioners. In view of various mechanisms developed in hopes of encouraging more active participations, we proposed a motivational model for PSVC contributions. Drawn on functional motivation theory and expectancy-value theory, we highlight the mediating role of individual motivations in the relationships between characteristics of PSVCs and knowledge contribution intention. By taking both self-interest and public-good perspectives, we identify eight individual motivations to knowledge contribution in the context of PSVCs. They are active learning, self-enhancement, reciprocity, reputation, enjoyment of helping others, self-protection, moral obligation and advancement of virtual community motive. Besides, we also identify four major mechanisms in PSVCs that are associated with members' motivations: codification, identity management, virtual community norms and communication methods.
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.
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.
14474115
Active Measurement of Routing Table in Kad
As the first DHT implemented in real applications and involving millions of simultaneous users, all aspects of Kad must be analyzed and measured carefully. This paper focuses on measuring the routing table of Kad in eMule/aMule. We present and analyze the availability and stability of routing table by crawling actively. 
 
We find the phenomenon of ID repetition in Kad that many peers use a same ID simultaneously, which will decrease the performance of routing and then reduce the availability of routing table. The connection availability of global routing table is relatively low, the average of which is about 64.9%. Connection availability influences the efficiency of searching and routing in Kad network directly.