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
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.
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.
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.
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.
17966080
User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval
Understanding and learning the subjective aspect of humans in Content-Based Image Retrieval has been an active research field during the past few years. However, how to effectively discover users' concept patterns when there are multiple visual features existing in the retrieval system still remains a big issue. In this paper, we propose a multimedia data mining framework that incorporates Multiple Instance Learning into the user relevance feedback in a seamless way to discover the concept patterns of users, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of users. This underlying mapping can be progressively discovered through the feedback and learning procedure. The role user plays in the retrieval system is to guide the system mining process to his/her own focus of attention. The retrieval performance is tested under a couple of conditions.
34951978
A Top-Down Fingerprint Image Enhancement Method Based on Fourier Analysis
We present a top-down fingerprint image enhancement method which is based on Fourier analysis Our method takes advantage of two filtering methods in Fourier domain proposed previously and employs a top-down iteration filtering technique, which enables the enhancement procedure not to rely severely on an accurate estimation of ridge frequency and orientation and also enables us to make use of more than the local information in local fingerprint filtering Consequently, our method is robust to handle low quality fingerprint images Results of enhancement are presented for some representative fingerprint images.
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.
38318651
Parallel Virtual Screening for Prediction of Antimicrobial Activity Using Fourier Transforms
18178451
n-Tuple Coloring of Planar Graphs with Large Odd Girth
Abstract. The main result of the papzer is that any planar graph with odd girth at least 10k−7 has a homomorphism to the Kneser graph Gk2k+1, i.e. each vertex can be colored with k colors from the set {1,2,…,2k+1} so that adjacent vertices have no colors in common. Thus, for example, if the odd girth of a planar graph is at least 13, then the graph has a homomorphism to G25, also known as the Petersen graph. Other similar results for planar graphs are also obtained with better bounds and additional restrictions.
15778343
Smart rebinning for compression of concentric mosaics
Concentric mosaics offer a quick solution to construct a virtual copy of a real environment, and navigate in the virtual environment. However, the huge amount of data associated with concentric mosaics is a heavy burden for its application. A 3D wavelet transform-based compressor has been proposed in previous work to compress the concentric mosaics. In this paper, we greatly improve the performance of the 3D wavelet coder with a data rearrangement mechanism called “smart rebinning”. The proposed scheme first aligns the concentric mosaic image shots along the horizontal direction and then rebins the shots into multi-perspective panoramas. Smart rebinning greatly improves the cross shot correlation and enables the coder to better explore the redundancy among shots. Experimental results show that the performance of the 3D wavelet coder improves an average of 4.3dB with the use of smart rebinning. The proposed coder outperforms MPEG-2 coding of concentric mosaics by an average of 3.7dB.