9405868
Mining Personal Image Collection for Social Group Suggestion
Popular photo-sharing sites have attracted millions of people and helped construct massive social networks in cyberspace. Different from traditional social relationship, users actively interact within groups where common interests are shared on certain types of events or topics captured by photos and videos. Contributing images to a group would greatly promote the interactions between users and expand their social networks. In this work, we intend to produce accurate predictions of suitable photo-sharing groups from a user's images by mining images both on the Web and in the user’s personal collection. To this end, we designed a new approach to cluster popular groups into categories by analyzing the similarity of groups via SimRank. Both visual content and its annotations are integrated to understand the events or topics depicted in the images. Experiments on real user images demonstrate the feasibility of the proposed approach.
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
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
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
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.
782382
A Lightweight Mechanism to Mitigate Application Layer DDoS Attacks
Application layer DDoS attacks, to which network layer solutions is not applicable as attackers are indistinguishable based on packets or protocols, prevent legitimate users from accessing services. In this paper, we propose Trust Management Helmet (TMH) as a partial solution to this problem, which is a lightweight mitigation mechanism that uses trust to differentiate legitimate users and attackers. Its key insight is that a server should give priority to protecting the connectivity of good users during application layer DDoS attacks, instead of identifying all the attack requests. The trust to clients is evaluated based on their visiting history, and used to schedule the service to their requests. We introduce license, for user identification (even beyond NATs) and storing the trust information at clients. The license is cryptographically secured against forgery or replay attacks. We realize this mitigation mechanism and implement it as a Java package and use it for simulation. Through simulation, we show that TMH is effective in mitigating session flooding attack: even with 20 times number of attackers, more than 99% of the sessions from legitimate users are accepted with TMH; whereas less than 18% are accepted without it.
123789669
H ∞ control with parametric Lyapunov functions
A new control synthesis approach is proposed for linear parametrically varying (LPV) systems using parameter-dependent quadratic Lyapunov functions. The synthesis problem is formulated via a set of linear matrix inequalities (LMIs). When solved, parameter-dependent controllers are obtained that stabilize the LPV systems and achieve guaranteed performance in an L2-gain sense. The new approach includes the case of parameter-independent Lyapunov functions, and as a result provides additional flexibility in the control design.
16398028
A Detection and Offense Mechanism to Defend Against Application Layer DDoS Attacks
Application layer DDoS attacks, which are legitimate in packets and protocols, gradually become a pressing problem for commerce, politics and military. We build an attack model and characterize layer-7 attacks into three classes: session flooding attacks, request flooding attacks and asymmetric attacks. We proposed a mechanism named as DOW (defense and offense wall), which defends against layer-7 attacks using combination of detection technology and currency technology. An anomaly dete-ction method based on K-means clustering is introduced to detect and filter request flooding attacks and asymmetric attacks. To defend against session-flooding attacks, we propose an encouragement model that uses client's session rate as currency. Detection model drops suspicious sessions, while currency model encourages more legitimate sessions. By collaboration of these two models, normal clients could gain higher service rate and lower delay of response time.
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.
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.