12998313
An affinity-based image retrieval system for multimedia authoring and presentation
In this demonstration, we present an image retrieval system to support multimedia authoring and presentation. An affinity-based mechanism, Markov Model Mediator (MMM), is used as the search engine for the system, which utilizes both the low-level image features and the learned high-level concepts via user access patterns and access frequencies. This system is one of the major components of MediaManager, a distributed multimedia management system developed by us. Both retrieval and learning facilities are supported in this system. The retrieval system also provides input information to the Multimedia Augmented Transition Network (MATN) environment for multimedia authoring and presentation.
1252952
Information reuse and system integration in the development of a hurricane simulation system
This paper presents our effort in designing and implementing an advanced hurricane simulation system on the platform of the World Wide Web for the purpose of supporting decisional processes and hazard mitigation. The development of the system is based on amalgamated methodologies which come from diverse disciplines and is an integration of a variety of techniques. Superior to other similar systems in hurricane study, our system assembles and utilizes information and techniques in a more flexible and robust manner by taking advantage of system layering technique, component modeling strategy and database technologies. Preliminary experiments have been conducted to demonstrate the performance of the system.
10875476
Affinity relation discovery in image database clustering and content-based retrieval
In this paper, we propose a unified framework, called <i>Markov Model Mediator</i> (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories.
9847925
A multimedia semantic model for RTSP-based multimedia presentation systems
An abstract multimedia semantic model called the multimedia augmented transition network (MATN) model is proposed to model the RTSP-based (real-time streaming protocol) multimedia presentation systems. RTSP provides a set of methods to support the functionality of "VCR-like" remote control on the client side so that users are allowed to control playback of the streaming media. The MATN model is used to model RTSP actions as well as temporal relations and synchronization control of the media streams. The advantages of using the MATN model are its simplicity, effectiveness, and ease of modification. Furthermore, a formal definition of the MATN model and modeling of RTSP actions using the MATN model are presented in detail.
5113927
An unsupervised segmentation framework for texture image queries
In this paper a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.
5598037
Supporting Information Reuse for Video Data Indexing by Using Color Information
In this paper, an object tracking framework assisted by image segmentation and color indexing techniques to support information reuse for video data is proposed. In order to obtain more accurate results of object tracking, a color indexing method called Color Label Criteria (CLC) is adopted to relax some of the restrictions and assumptions in object tracking in our previous work with low additional cost. Our experiment results on a real video sequence show that the CLC color indexing method is very helpful to achieve more robust object tracking.
29297629
An Application of Fuzzy Cluster analysis to Matchmaking in Middle Agents
14018437
Parallel SOR Iterative Algorithms and Performance Evaluation on a Linux Cluster
Abstract : The successive over-relaxation (SOR) iterative method is an important solver for linear systems. In this paper, a parallel algorithm for the red-black SOR method with domain decomposition is investigated. The parallel SOR algorithm is designed by combining the traditional red-black SOR and row block domain decomposition technique, which reduces the communication cost and simplifies the parallel implementation. Two other iterative methods, Jacobi and Gauss-Seidel (G-S), ate also implemented in parallel for comparison. The three parallel iterative algorithms are implemented in C and MPI (Message Passing Interface) for solving the Dirichlet problem on a Linux cluster with eight dual processor 2.6ghz 32 bit Intel Xeons, totaling 16 processors. The performances of the three algorithms are evaluated in terms of speedup and efficiency.
3978452
The Interaction Between Window Adjustment Strategies and Queue Management Schemes
In this paper, we investigate extensively the joint network dynamics with different AIMD window-adjustment parameters on end-hosts, and different queue management schemes (i.e. DropTail vs. RED) in routers. We reveal that with DropTail buffer, although smooth TCPs causes less queuing-delay jitter, its average queuing delay is significantly higher than that of responsive TCPs. The direct implication of this discovery is that when mobile users of media-streaming and short messages share a bottleneck link, the energy consumption for sending short messages can increase severely if media-streaming users adopt smooth TCPs. With RED, on the other hand, smooth TCPs not only lead to smaller queue oscillation, the average/max queue length is smaller as well.
9803776
Semantic propagation from relevance feedbacks
Relevance feedback has been a very useful tool to enhance the performance of content-based information retrieval (CBIR) systems. To fully make use of the precious user feedback provided to a system, we propose an approach named semantic propagation, which reveals the deep semantic relationships among objects in the database, given a set of relevance feedbacks between object pairs. In particular, we present two semantic propagation algorithms that are applicable to CIBR systems with a feature vector space model and a general metric space model, respectively. Experiments on a 3D model retrieval system and a logo image retrieval system are performed to show the effectiveness of the proposed methods.
12091703
Indexing and retrieval of 3D models aided by active learning
We demonstrate a system for indexing and retrieval of 3D models aided by active learning. We propose a new set of region-based features for 3D models. Each model is treated as a solid volume with a uniform density. Features such as the volume-surface ratio, the moment invariants and the Fourier transform coefficients are efficiently calculated from the mesh model directly. Comparable retrieval performance is achieved with other features such as the cord histogram, the 3D shape spectrum, etc. To further improve the performance, we incorporate hidden annotation into our system. We propose to use active learning to improve the annotation efficiency. We show that with active learning, the system can perform better than random annotation, and the retrieval result improves rapidly with the number of annotated samples. Moreover, relevance feedback is included in the system and combined with active learning, which provides better user-adoptive retrieval results.