15205125
Fuzzy color histogram and its use in color image retrieval
A conventional color histogram (CCH) considers neither the color similarity across different bins nor the color dissimilarity in the same bin. Therefore, it is sensitive to noisy interference such as illumination changes and quantization errors. Furthermore, CCHs large dimension or histogram bins requires large computation on histogram comparison. To address these concerns, this paper presents a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixel's color associated to all the histogram bins through fuzzy-set membership function. A novel and fast approach for computing the membership values based on fuzzy c-means algorithm is introduced. The proposed FCH is further exploited in the application of image indexing and retrieval. Experimental results clearly show that FCH yields better retrieval results than CCH. Such computing methodology is fairly desirable for image retrieval over large image databases.
5983751
Fusion of color and infrared video for moving human detection
We approach the task of human silhouette extraction from color and thermal image sequences using automatic image registration. Image registration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, the moving people in a static scene provide cues to address this problem. In this paper, we propose a hierarchical scheme to automatically find the correspondence between the preliminary human silhouettes extracted from synchronous color and thermal image sequences for image registration. Next, we discuss strategies for probabilistically combining cues from registered color and thermal images for improved human silhouette detection. It is shown that the proposed approach achieves good results for image registration and human silhouette extraction. Experimental results also show a comparison of various sensor fusion strategies and demonstrate the improvement in performance over non-fused cases for human silhouette extraction.
2796750
Pattern-Based Specification and Validation of Web Services Interaction Properties
There have been significant efforts in providing semantic descriptions for Web services, including the approach as exemplified by OWL-S. Part of the semantic description in OWL-S is about the interaction process of the service concerned, and adopts a procedural programming style. We argue that this style of description for service interactions is not natural to publishing service behavior properties from the viewpoint of facilitating third-party service composition and analysis. In this paper, we introduce a declarative approach that better supports the specification and use of service interaction properties in the service description and composition process. This approach uses patterns to describe the interaction behavior of a service as a set of constraints. As such, it supports the incremental description of a service’s interaction behavior from the service developer’s perspective, and the easy understanding and analysis of the interaction properties from the service user’s perspective. We also introduce a framework and tool support for monitoring and checking the conformance of the service’s run-time interactions against its specified interaction properties, to test whether the service is used properly and whether the service fulfils its behavioral obligations.
43532686
AIM: Approximate Intelligent Matching for Time Series Data
Time-series data mining presents many challenges due to the intrinsic large scale and high dimensionality of the data sets. Subsequence similarity matching has been an active research area driven by the need to analyse large data sets in the financial, biomedical and scientific databases. In this paper, we investigate an intelligent subsequence similarity matching of time series queries based on efficient graph traversal. We introduce a new problem, the approximate partial matching of a query sequence in a time series database. Our system can address such queries with high specificity and minimal time and space overhead. The performance bottleneck of the current methods were analysed and we show our method can improve the performance of the time series queries significantly. It is general and flexible enough to find the best approximate match query without specifying a tolerance Ɛ parameter.
10084934
CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets
Association mining may often derive an undesirably large set of frequent itemsets and association rules. Recent studies have proposed an interesting alternative: mining frequent closed itemsets and their corresponding rules, which has the same power as association mining but substantially reduces the number of rules to be presented. In this paper, we propose an e cient algorithm, CLOSET, for mining closed itemsets, with the development of three techniques: (1) applying a compressed, frequent pattern tree FP-tree structure for mining closed itemsets without candidate generation, (2) developing a single pre x path compression technique to identify frequent closed itemsets quickly, and (3) exploring a partition-based projection mechanism for scalable mining in large databases. Our performance study shows that CLOSET is e cient and scalable over large databases, and is faster than the previously proposed methods.
2940771
Graph indexing: a frequent structure-based approach
Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. In this paper, we investigate the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Different from the existing path-based methods, our approach, called gIndex, makes use of frequent substructure as the basic indexing feature. Frequent substructures are ideal candidates since they explore the intrinsic characteristics of the data and are relatively stable to database updates. To reduce the size of index structure, two techniques, size-increasing support constraint and discriminative fragments, are introduced. Our performance study shows that gIndex has 10 times smaller index size, but achieves 3--10 times better performance in comparison with a typical path-based method, GraphGrep. The gIndex approach not only provides and elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit form data mining, especially frequent pattern mining. Furthermore, the concepts developed here can be applied to indexing sequences, trees, and other complicated structures as well.
1503756
Using Bayesian belief networks for change impact analysis in architecture design
Research into design rationale in the past has focused on argumentation-based design deliberations. These approaches cannot be used to support change impact analysis effectively because the dependency between design elements and decisions are not well represented and cannot be quantified. Without such knowledge, designers and architects cannot easily assess how changing requirements and design decisions may affect the system. In this article, we introduce the Architecture Rationale and Element Linkage (AREL) model to represent the causal relationships between architecture design elements and decisions. We apply Bayesian Belief Networks (BBN) to AREL, to capture the probabilistic causal relationships between design elements and decisions. We employ three different BBN-based reasoning methods to analyse design change impact: predictive reasoning, diagnostic reasoning and combined reasoning. We illustrate the application of the BBN modelling and change impact analysis methods by using a partial design of a real-world cheque image processing system. To support its implementation, we have developed a practical, integrated tool set for the architects to use.