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.
2279134
Toward Robust Distance Metric Analysis for Similarity Estimation
In this paper, we present a general guideline to establish the relation between a distribution model and its corresponding similarity estimation. A rich set of distance metrics, such as harmonic distance and geometric distance, is derived according to Maximum Likelihood theory. These metrics can provide a more accurate feature model than the conventional Euclidean distance (SSD) and Manhattan distance (SAD). Because the feature elements are from heterogeneous sources and may have different influence on similarity estimation, the assumption of single isotropic distribution model is often inappropriate. 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. We experiment with different distance metrics for similarity estimation and compute the accuracy of different methods in two applications: stereo matching and motion tracking in video sequences. 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.
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.
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.
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.
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.
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.