25362745
A Multimedia Data Mining Framework: Mining Information from Traffic Video Sequences
The analysis and mining of traffic video sequences to discover important but previously unknown knowledge such as vehicle identification, traffic flow, queue detection, incident detection, and the spatio-temporal relations of the vehicles at intersections, provide an economic approach for daily traffic monitoring operations. To meet such demands, a multimedia data mining framework is proposed in this paper. The proposed multimedia data mining framework analyzes the traffic video sequences using background subtraction, image/video segmentation, vehicle tracking, and modeling with the multimedia augmented transition network (MATN) model and multimedia input strings, in the domain of traffic monitoring over traffic intersections. The spatio-temporal relationships of the vehicle objects in each frame are discovered and accurately captured and modeled. Such an additional level of sophistication enabled by the proposed multimedia data mining framework in terms of spatio-temporal tracking generates a capability for automation. This capability alone can significantly influence and enhance current data processing and implementation strategies for several problems vis-à-vis traffic operations. Three real-life traffic video sequences obtained from different sources and with different weather conditions are used to illustrate the effectiveness and robustness of the proposed multimedia data mining framework by demonstrating how the proposed framework can be applied to traffic applications to answer the spatio-temporal queries.
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.
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.
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.
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.
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.
6853058
Markov chain correlation based clustering of gene expression data
An efficient Markov chain correlation based clustering method (MCC) has been proposed for clustering gene expression data. The gene expression data is first normalized and Markov chains (MC) are constructed from the dynamics of the gene expressions, in which the behavior of the genes at each step of the experiment can be taken into account. Based on the correlation of one-step Markov chain transition probabilities, an agglomerative method is employed to group the series that have similar behavior at each point. The proposed MCC clustering method has been applied to four gene expression datasets to obtain a number of clusters. The results show that the MCC method outperforms the commonly used K-means method and produces clusters that are more meaningful in terms of the similarity of the grouped genes. Another advantage of the proposed method over the existing clustering methods is that the knowledge of the group number is not required.
15177304
A new method of color image segmentation based on intensity and hue clustering
A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and the center in the hue space, based on which the hue-clustering algorithm is implemented. Utilised in medical image processing, the new method gives a good performance.
6709890
An agent-based hybrid framework for database mining
While knowledge discovery in databases (KDD) is defined as an iterative sequence of the following steps: data pre-processing, data mining, and post data mining, a significant amount of research in data mining has been done, resulting in a variety of algorithms and techniques for each step. However, a single data-mining technique has not been proven appropriate for every domain and data set. Instead, several techniques may need to be integrated into hybrid systems and used cooperatively during a particular data-mining operation. That is, hybrid solutions are crucial for the success of data mining. This paper presents a hybrid framework for identifying patterns from databases or multi-databases. The framework integrates these techniques for mining tasks from an agent point of view. Based on the experiments conducted, putting different KDD techniques together into the agent-based architecture enables them to be used cooperatively when needed. The proposed framework provides a highly flexible and robust data-mining platform and the resulting systems demonstrate emergent behaviors although it does not improve the performance of individual KDD techniques.
11462933
On flows in bidirected graphs
Bouchet conjectured that every bidirected graph which admits a nowhere-zero bidirected flow will admit a nowhere-zero bidirected 6-flow [A. Bouchet, Nowhere-zero integer flows on a bidirected graph, J. Combin. Theory Ser. B 34 (1983) 279-292]. He proved that this conjecture is true with 6 replaced by 216. Zyka proved in his Ph.D dissertation that it is true with 6 replaced by 30. Khelladi proved it is true with 6 replaced with 18 for 4-connected graphs [A. Khelladi, Nowhere-zero integer chains and flows in bidirected graphs, J. Combin. Theory Ser. B 43 (1987) 95-115]. In this paper, we prove that Bouchet's conjecture is true for 6-edge connected bidirected graphs.
6431749
Reasoning with Multimedia Information Using Symbolic Projection
In many multimedia application systems, it is not the final goal to retrieve the relevant multimedia information from different multimedia information sources. Rather, post-processing of the retrieved multimedia information is needed. For example, the retrieved information is used as "known facts". The systems will do some reasoning to obtain further conclusions based on these multimedia form "known facts". We call this reasoning with multimedia information. Most current research work in multimedia information processing is focused on multimedia information retrieval, but post-processing the retrieved information is more or less ignored. This paper explores the way to tackle this problem by using symbolic projection. A case study of reasoning with still image information is presented. Some extensions to symbolic projection-introducing auxiliary pictorial objects in symbolic pictures that need to be processed-are discussed. We expect this paper will stimulate further research on this important but ignored topic.