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.
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.
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
40826674
Boosting Multiple Classifiers Constructed by Hybrid Discriminant Analysis
In this paper, a set of hybrid dimension reduction schemes is constructed by unifying principal component analysis (PCA) and linear discriminant analysis (LDA) in a single framework. PCA compensates LDA for singular scatter matrix caused by small set of training samples and increases the effective dimension of the projected subspace. Generalization of hybrid analysis is extended to other discriminant analysis such as multiple discriminant analysis (MDA), and the recent biased discriminant analysis (BDA), and other hybrid pairs. In order to reduce the search time to find the best single classifier, a boosted hybrid analysis is proposed. Our scheme boosts both the individual features as well as a set of weak classifiers. Extensive tests on benchmark and real image databases have shown the superior performance of the boosted hybrid analysis.
17892376
Validation of ASIP Architecture Description
Validation is one of the most complex and expensive tasks in current Application Specific Instruction Set Processors (ASIP) design process. Many existing approaches employ a multiple-level approach to efficiently design and verify ASIP design. This paper presents a novel extended timed Petri net model called HDPN-Hardware Design based-on Petri Net to model systems at multiple levels, and introduces a verification scheme based on HDPN to satisfy the requirement of Design Space Exploration (DSE). This paper focuses on formal modeling and verification ASIP architecture. And a DLX pipelined processor is presented to demonstrate the validity and usage of this 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.
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.
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.
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.