11885669
A decision tree-based multimodal data mining framework for soccer goal detection
We propose a new multimedia data mining framework for the extraction of soccer goal events in soccer videos by using combined multimodal analysis and decision tree logic. The extracted events can be used to index the soccer videos. We first adopt an advanced video shot detection method to produce shot boundaries and some important visual features. Then, the visual/audio features are extracted for each shot at different granularities. This rich multimodal feature set is filtered by a pre-filtering step to clean the noise as well as to reduce the irrelevant data. A decision tree model is built upon the cleaned data set and is used to classify the goal shots. Finally, the experimental results demonstrate the effectiveness of our framework for soccer goal extraction.
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.
16103789
A flexible image retrieval and multimedia presentation management system for multimedia databases
In today's fast-growing information age, multimedia data is becoming more and more common in daily applications, however this data is nearly useless if there is no computer-aided browsing, searching, and retrieving mechanism to obtain the desired contents. In this demonstration, we present a multimedia query and presentation management system for multimedia databases. It uses an unsupervised segmentation method for image and video feature extraction. A presentation design interface systems is provided that allows query results to be used to create multimedia presentations.
9616437
Image database retrieval utilizing affinity relationships
Recent research effort in Content-Based Image Retrieval (CBIR) focuses on bridging the gap between low-level features and high-level semantic contents of images as this gap has become the bottleneck of CBIR. In this paper, an effective image database retrieval framework using a new mechanism called the Markov Model Mediator (MMM) is presented to meet this demand by taking into consideration not only the low-level image features, but also the high-level concepts learned from the history of user's access pattern and access frequencies on the images in the database. Also, the proposed framework is efficient in two aspects: 1) Overhead for real-time training is avoided in the image retrieval process because the high-level concepts of images are captured in the off-line training process. 2) Before the exact similarity matching process, Principal Component Analysis (PCA) is applied to reduce the image search space. A training subsystem for this framework is implemented and integrated into our system. The experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results from image databases.
16767212
A Robust Method for Biological Sequence Clustering
In this paper, we proposed a two-phase hybrid method for biological sequence clustering, which combines the strengths of the hierarchical agglomerative clustering methods and the partition clustering methods. In phase I, the hybrid method uses the hierarchical agglomerative clustering algorithm to pre-cluster the aligned sequences, while in the second phase it takes the pre-clustering result as the initial partition for the profile hidden Markov models (HMMs) based k-means partition clustering method. Such initial partitions (generated from phase I), as against random initial partitions, are usually more reasonable and thus can avoid the inconsistency problem in the partition clustering methods due to the randomness in initial partitions. In addition, the inaccuracy of the hierarchical agglomerative clustering methods can be compensated by the profile HMM based k-means partition clustering since the latter is model-based and can better describe the dynamic properties of the data in a cluster. Experiments on a molecular sequence dataset demonstrate the effectiveness and the efficiency of the proposed hybrid clustering algorithm
2023360
A reliable Web-based system for hurricane analysis and simulation
In this paper, we present our research and development efforts toward a reliable, large-scale, Web-based system for hurricane analysis and simulation. The major contributions of this system lie in the following three aspects: (1) it supports the multidisciplinary research efforts in predicting and simulating the hurricane damages and insured losses; (2) it is a large-scale, distributed, Web-based system, which allows both the professional and general users to conduct computational intensive on-line analysis simultaneously and to share and exchange the information; and (3) database management techniques are employed in our system to manage the huge amount of hurricane-related historical and simulated data.
25611508
Regression Nearest Neighbor in Face Recognition
In this paper, we introduce a regression nearest neighbor framework for general classification tasks. To alleviate potential problems caused by nonlinearity, we propose a kernel regression nearest neighbor (KRNN) algorithm and its convex counterpart (CKRNN) as two specific extensions of nearest neighbor algorithm and present a fast and useful kernel selection method correspondingly. Comprehensive analysis and extensive experiments are used to demonstrate the effectiveness of our methods in real face datasets
1085378
Profiling alternatively spliced mRNA isoforms for prostate cancer classification
BackgroundProstate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.ResultsAs many as 464 splice isoforms from more than 200 genes are differentially regulated in tumors at a false discovery rate (FDR) of 0.05. Remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mRNA level. A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%. It is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account.ConclusionThese results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays.
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.
1748998
Minimum Register Instruction Scheduling: A New Approach for Dynamic Instruction Issue Processors
Modern superscalar architectures with dynamic scheduling and register renaming capabilities have introduced subtle but important changes into the tradeoffs between compile-time register allocation and instruction scheduling. In particular, it is perhaps not wise to increase the degree of parallelism of the static instruction schedule at the expense of excessive register pressure which may result in additional spill code. To the contrary, it may even be beneficial to reduce the register pressure at the expense of constraining the degree of parallelism of the static instruction schedule. This leads to the following interesting problem: given a data dependence graph (DDG) G, can we derive a schedule S for G that uses the least number of registers ? 
 
In this paper, we present a heuristic approach to compute the near-optimal number of registers required for a DDG G (under all possible legal schedules). We propose an extended list-scheduling algorithm which uses the above number of required registers as a guide to derive a schedule for G that uses as few registers as possible. Based on such an algorithm, an integrated approach for register allocation and instruction scheduling for modern superscalar architectures can be developed.
23191981
Cycles of all lengths in arc-3-cyclic semicomplete digraphs
Abstract Let D be an arc-3-cyclic, semicomplete digraph and uv be an arc of D contained in a cycle of length r . If vu ∉ A ( D ) then the arc uv is contained in cycles of length h : 3 ⩽ h ⩽ r , or if δ + ( D ), δ − ( D ) ⩾ 3 then the arc uv is contained in cycles of length h : 6 ⩽ h ⩽ r . Also included in this paper is a very useful crossing arc theorem .