8860337
Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression
Photo community sites such as Flickr and Picasa Web Album host a massive amount of personal photos with millions of new photos uploaded every month. These photos constitute an overwhelming source of images that require effective management. There is an increasingly imperative need for semantic annotation of these web images. This paper addresses the problem by considering two kinds of annotation: semantic annotation and geographic annotation. Both are useful for image search and retrieval and for facilitating communities and social networks. This paper proposes a novel method of Logistic Canonical Correlation Regression (LCCR) for the annotation task. This model exploits the canonical correlation between heterogeneous features and an annotation lexicon of interest, and builds a generalized annotation engine based on canonical correlations in order to produce enhanced annotation for web images. We validate the effectiveness of our algorithm using a dataset of over 380,000 images tagged with GPS coordinates.
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.
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
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
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
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.
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.
522561
POLUS: A POwerful Live Updating System
This paper presents POLUS, a software maintenance tool capable of iteratively evolving running software into newer versions. POLUS's primary goal is to increase the dependability of contemporary server software, which is frequently disrupted either by external attacks or by scheduled upgrades. To render POLUS both practical and powerful, we design and implement POLUS aiming to retain backward binary compatibility, support for multithreaded software and recover already tainted state of running software, yet with good usability and very low runtime overhead. To demonstrate the applicability of POLUS, we report our experience in using POLUS to dynamically update three prevalent server applications: vsftpd, sshd and apache HTTP server. Performance measurements show that POLUS incurs negligible runtime overhead: a less than 1% performance degradation (but 5% for one case). The time to apply an update is also minimal.
16850899
Heavy tails, generalized coding, and optimal Web layout
This paper considers Web layout design in the spirit of source coding for data compression and rate distortion theory, with the aim of minimizing the average size of files downloaded during Web browsing sessions. The novel aspect here is that the object of design is layout rather than codeword selection, and is subject to navigability constraints. This produces statistics for file transfers that are heavy tailed, completely unlike standard Shannon theory, and provides a natural and plausible explanation for the origin of observed power laws in Web traffic. We introduce a series of theoretical and simulation models for optimal Web layout design with varying levels of analytic tractability and realism with respect to modeling of structure, hyperlinks, and user behavior. All models produce power laws which are striking both for their consistency with each other and with observed data, and their robustness to modeling assumptions. These results suggest that heavy tails are a permanent and ubiquitous feature of Internet traffic, and not an artifice of current applications or user behavior. They also suggest new ways of thinking about protocol design that combines insights from information and control theory with traditional networking.