14830218
Leveraging probabilistic season and location context models for scene understanding
Recent research has shown the power of context-aware scene understanding in bridging the semantic gap between high-level semantic concepts and low-level image features. In this paper, we present a new method to exploit nonvisual context information from the season and location proximity in which pictures were taken to facilitate region (object) annotation in consumer photos. Our method does not require precise time and location from the capture device or user input. Instead, it learns from rough location (e.g., states in the US) and time (e.g., seasons) information, which can be obtained through picture metadata automatically or through minimal user input (e.g., grouping). In addition, the visual context within the image is obtained by analyzing the spatial relationships between different regions (objects) in the scene. Both visual and nonvisual context information are fused using a probabilistic graphical model to improve the accuracy of object region recognition. Our method has been evaluated on a database that consists of over 10,000 regions in more than 1000 images collected from both the Web and consumers. Experimental results show that incorporating the season and location context significantly improves the performance of region recognition.
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.
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
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
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.
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.
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.
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.
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.