2023739
Joint video scene segmentation and classification based on hidden Markov model
Video classification and segmentation are fundamental steps for efficient accessing, retrieval and browsing of large amounts of video data. We have developed a scene classification scheme using a hidden Markov model (HMM) based classifier. By utilizing the temporal behaviors of different scene classes, the HMM classifier can effectively classify video segments into one of the pre-defined scene classes. In this paper, we describe two approaches for joint video classification and segmentation based on a HMM, which works by searching for the most likely class transition path utilizing the dynamic programming technique.
17193716
Detecting news reporting using audio/visual information
This paper proposes an integrated approach to discriminate news reporting from everything else in broadcast news data based on both audio and visual information. The separation of news reporting segments from others not only can provide useful indices for video streams but also serves as a pre-processing step for tasks such as speaker identification and speech recognition so that only speech segments are passed on for further processing. A set of audio and visual features are adopted that aim at capturing the intrinsic properties of the underlying classes. Four types of classifiers (threshold fuzzy, Gaussian Mixture Model based, and Support Vector Machine) are tested. Some of the experimental results are presented and discussed in the paper.
28562
Content-based indexing and retrieval-by-example in audio
This paper presents a system for audio content-based indexing and retrieval-by-example. An unsupervised method to automatically index the audio content is proposed. To realize a retrieval system where the on-line query can be performed efficiently, a new parametric distance metric is used to allow faster identification of the audio segments that are similar to the given query example. Preliminary experimental results show that the proposed new approach can serve as an effective means in content based indexing and retrieval in audio streams.
11845096
Integration of audio and visual information for content-based video segmentation
A video sequence usually consists of separate scenes, and each scene includes many shots. For video understanding purposes, it is most important to detect scene breaks. To analyze the content of each scene, detection of shot breaks is also required. Usually, a scene break is associated with a simultaneous change of image, motion, and audio characteristics, while a shot break is only accompanied with changes in image or motion or both. We propose to use audio information along with image and motion information to accomplish segmentation at different levels. Promising results have been obtained with videos digitized from TV programs.
4908568
Insignificant shadow detection for video segmentation
To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance.
3758350
An Analytical Approach to the Performance Evaluation of Master-Slave Computational Models
Abstract Many stochastic models and analysis techniques have been proposed in the literature during the last two decades for the performance evaluation of parallel and distributed systems. However, few of them are directly applicable to practical systems which are generally too complex. In this paper, we analyze performances of the master–slave computational model, one of the most commonly used models in parallel and distributed computations. We propose a hybrid analytical approach by using techniques from the theories of both stochastic task graphs and queueing networks. We apply this method to the analysis of a computational chemistry application running on a Transputer based system. The proposed method turns out to be not only very efficient in time for large systems but also very accurate compared to measuring.
17231470
Dynamic scheduling of parallel computations
Structures of parallel programs are usually represented by task graphs in the scheduling literature. Such graphs are sometimes obtained at compile time. In many other cases, however, they can be determined only at run time. In this paper, we consider the scheduling of parallel computations whose task graphs are generated at run time. We analyze the case where the task graph is a random out-tree. When the number of offspring of a task has a geometric distribution whose parameter is decreasing and convex in the level, then the breadth-first policy stochastically minimizes the makespan. If, however, this parameter is increasing and concave, then the depth-first policy stochastically minimizes the makespan.
10909052
Trust in the preservation of digital information
An institutional guarantee can help address the major hurdle of digital preservation---winning people's trust.
12789585
A Secure Agent Architecture for Sensor Networks
Advances in sensor and wireless technologies will enable thousands, or even millions of small but smart sensors to be deployed for a wide range of monitoring applications. The sensor networks must be dynamically configurable and adaptive in order to response actively to events. Security must also be built into sensor networks at the very initial design stage to prevent any potential threats. This paper presents a design of a generic software architecture based on tiny active agents for sensor networks. The architecture has built-in security components. This architecture will significantly benefit the dynamic and secure deployment and configuration tasks of large sensor networks for various applications.