2536834
Extensible detection and indexing of highlight events in broadcasted sports video
Content-based indexing is fundamental to support and sustain the ongoing growth of broadcasted sports video. The main challenge is to design extensible frameworks to detect and index highlight events. This paper presents: 1) A statistical-driven event detection approach that utilizes a minimum amount of manual knowledge and is based on a universal scope-of-detection and audio-visual features; 2) A semi-schema-based indexing that combines the benefits of schema-based modeling to ensure that the video indexes are valid at all time without manual checking, and schema-less modeling to allow several passes of instantiation in which additional elements can be declared. To demonstrate the performance of the events detection, a large dataset of sport videos with a total of around 15 hours including soccer, basketball and Australian football is used.
6262105
A New Indexing Method for High Dimensional Dataset
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse of dimensionality” problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of “curse of dimensionality”; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.
39700388
Surface Spatial Index Structure of High-Dimensional Space
This paper proposes a spatial index structure based on a new space-partitioning method. Previous research proposed various high dimensional index structures. However, when dimensionality becomes high, the effectiveness of the spatial index structure disappears. This problem is called the “curse of dimensionality”. This paper focuses on the fact that the volume of high dimensional space is mostly occupied by its surface and then proposes a new surface index structure. The utility of this new surface spatial index structure is illustrated through a series of experiments.
12775848
Analyzing Inconsistency Toward Enhancing Integration of Biological Molecular Databases
The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.
24794561
Finding Short Patterns to Classify Text Documents
Many classification methods have been proposed to find patterns in text documents. However, according to Occam's razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm
11734040
Analyzing Security Protocols Using Association Rule Mining
Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.
17642380
Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
This paper presents a new idea of modeling one-dimensional dynamic systems by higher-order ordinary differential equation (HODE) models in stead of by the ARMA models used in the traditional time series analysis. Accordingly, based on the idea of hybrid evolutionary modeling, we modify the HEMA algorithm in (Cao et al., 1998a; Cao et al., 1998b) to approach the modeling problem of HODEs for dynamic systems. Two applications of time series are used to demonstrate its effectiveness and advantages.
18395542
Adaptive Web Services Testing
Web services (WS) and service-oriented architecture (SOA) present a set of unique testing challenges. As services are distributed, it is necessary to test them using a distributed architecture. Furthermore, as these services may keep on changing, testing needs to be adaptive. This paper proposes an adaptive testing framework which can continuously learn and improve the built-in test strategies. The framework allows different test cases to be selected based on the recent test results. The framework also has a windowing mechanism to evaluate and select test cases.
16583184
Using cluster skeleton as prototype for data labeling
A new approach, designed for clustering data whose underlying distribution shapes are arbitrary, is presented. This study is concerned with the use of the skeleton of a cluster as its prototype, which can represent the cluster more closely than that of using a single data point. The given data set is then partitioned into those skeleton-represented clusters without any prior knowledge nor assumptions of hidden structures. A novel function called cluster characteristic function (CCF) has been constructed and the associated theorems have been proposed and proved that the proper number of clusters can be determined with the approach.
8440158
Improved information maximization based face and facial feature detection from real-time video and application in a multi-modal person identification system
In this paper an improved face detection method based on our previous information-based maximum discrimination approach is presented that maximizes the discrimination between face and non-face examples in a training set without using color or motion information. A short review of our previous method is given together with a description of a recent improvement of its detection speed. A person identification system has been developed that performs multi-modal person identification in real-time video based on this newly improved face detection method together with speaker identification.
16524073
Partitioning of temporal planning problems in mixed space using the theory of extended saddle points
We study the partitioning of temporal planning problems formulated as mixed-integer nonlinear programming problems, develop methods to reduce the search space of partitioned subproblems, and propose algorithms for resolving unsatisfied global constraints. The algorithms are based on the necessary and sufficient extended saddle-point condition for constrained local minimization developed in this paper. When compared with the MIPS planner in solving some PDDL2.1 planning problems, our distributed implementation of MIPS shows significant improvements in time and quality.