222927
Regularized color clustering in medical image database
A regularized color clustering algorithm is proposed to solve the color clustering problem in medical image database. By incorporating both measures of cluster separability and cluster compactness, regularized color clustering allows the automatic extraction of significant color groups with varying populations. Experimental results in different color spaces show that the regularized color clustering gives superior results in extracting significant distinct/abnormal color clusters without significant increases in cluster compactness. Furthermore, results of color clustering in different color spaces show that the LUV color space is more suitable for color clustering. Methods for selecting the regularization constants have also been suggested.
35216739
Image Smoothing Using Parametric Relaxation
Abstract A new image-smoothing algorithm which smooths the image while preserving discontinuities is developed. The method poses the smoothing problem as an estimation problem of the maximum a posteriori probability of a pixel having a certain gray level. A prior probability density function is assigned to each pixel from the raw data, which is an estimate of the gray-level uncertainty. The algorithm is an application of the random measure field in image processing. Contextual information from neighbors is used in refining the probability distribution of each pixel, which is a continuous version of the relaxation algorithm. The way of parametrizing the probability functions enables the derivation of an efficient iterative formula for updating the gray-level values.
1209241
Spectral Energy Minimization for Semi-supervised Learning
Data mining problems often involve a large amount of unlabeled data and there is often very limited known information on the dataset. In such scenario, semi-supervised learning can often improve classification performance by utilizing unlabeled data for learning. In this paper, we proposed a novel approach to semi-supervised learning as as an optimization of both the classification energy and cluster compactness energy in the unlabeled dataset. The resulting integer programming problem is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.
39036081
Tongue image matching using color content
This paper addresses the problem of color image matching in medical diagnosis. The color matching of tongue images in different color spaces with different metrics have been investigated and is reported in this article. Two new metrics namely, sorted metric and probabilistic combined metric, are proposed. Existing distance measurements in coordinate space do not satisfy the reflexivity axiom. That means, they are not the valid metrics. To overcome this limitation, the sorted metric in coordinate space is proposed in coordinate space. To improve the matching performance, a probabilistic combined metric is proposed based on the theory of combining classifier. These metrics are applied for the matching of tongue color images and the results are encouraging.
8216205
Dissimilarity learning for nominal data
Defining a good distance (dissimilarity) measure between patterns is of crucial importance in many classification and clustering algorithms. While a lot of work has been performed on continuous attributes, nominal attributes are more difficult to handle. A popular approach is to use the value difference metric (VDM) to define a real-valued distance measure on nominal values. However, VDM treats the attributes separately and ignores any possible interactions among attributes. In this paper, we propose the use of adaptive dissimilarity matrices for measuring the dissimilarities between nominal values. These matrices are learned via optimizing an error function on the training samples. Experimental results show that this approach leads to better classification performance. Moreover, it also allows easier interpretation of (dis)similarity between different nominal values.
46337154
Minimum cross entropy thresholding
Abstract The threshold selection problem is solved by minimizing the cross entropy between the image and its segmented version. The cross entropy is formulated in a pixel-to-pixel basis between the two images and a computationally attractive algorithm employing the histogram is developed. Without making a priori assumptions about the population distribution, this method provides an unbiased estimate of a binarized version of the image in an information theoretic sense.
16649809
Relative difference sets fixed by inversion and Cayley graphs
Using graph theoretical technique, we present a construction of a (30, 2, 29, 14)-relative difference set fixed by inversion in the smallest finite simple group--the alternating group A5. To our knowledge this is the first example known of relative difference sets in the finite simple groups with a non-trivial forbidden subgroup. A connection is then established between some relative difference sets fixed by inversion and certain antipodal distance-regular Cayley graphs. With the connection, several families of antipodal distance-regular Cayley graphs which are coverings of complete graphs are presented.
6241358
Executing SQL over encrypted data in the database-service-provider model
Rapid advances in networking and Internet technologies have fueled the emergence of the "software as a service" model for enterprise computing. Successful examples of commercially viable software services include rent-a-spreadsheet, electronic mail services, general storage services, disaster protection services. "Database as a Service" model provides users power to create, store, modify, and retrieve data from anywhere in the world, as long as they have access to the Internet. It introduces several challenges, an important issue being data privacy. It is in this context that we specifically address the issue of data privacy.There are two main privacy issues. First, the owner of the data needs to be assured that the data stored on the service-provider site is protected against data thefts from outsiders. Second, data needs to be protected even from the service providers, if the providers themselves cannot be trusted. In this paper, we focus on the second challenge. Specifically, we explore techniques to execute SQL queries over encrypted data. Our strategy is to process as much of the query as possible at the service providers' site, without having to decrypt the data. Decryption and the remainder of the query processing are performed at the client site. The paper explores an algebraic framework to split the query to minimize the computation at the client site. Results of experiments validating our approach are also presented.
8491927
Multiresolution genetic clustering algorithm for texture segmentation
Abstract This work plans to approach the texture segmentation problem by incorporating genetic algorithm and K-means clustering method within a multiresolution structure. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the lower levels so as to reduce the inherent class–position uncertainty and to improve the segmentation accuracy. The procedure is described as follows. In the first step, a quad-tree structure of multiple resolutions is constructed. Sampling windows of different sizes are utilized to partition the underlying image into blocks at different resolution levels and texture features are extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. While the select and mutate operators of the traditional genetic algorithm are adopted in this work, the crossover operator is replaced with K-means clustering method. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.
3458490
Supporting ranking and clustering as generalized order-by and group-by
The Boolean semantics of SQL queries cannot adequately capture the "fuzzy" preferences and "soft" criteria required in non-traditional data retrieval applications. One way to solve this problem is to add a flavor of "information retrieval" into database queries by allowing fuzzy query conditions and flexibly supporting grouping and ranking of the query results within the DBMS engine. While ranking is already supported by all major commercial DBMSs natively, support of flexibly grouping is still very limited (i.e., group-by).
 In this paper, we propose to generalize group-by to enable flexible grouping (clustering specifically) of the query results. Different from clustering in data mining applications, our focus is on supporting efficient clustering of Boolean results generated at query time. Moreover, we propose to integrate ranking and clustering with Boolean conditions, forming a new type of ClusterRank query to allow structured data retrieval. Such an integration is nontrivial in terms of both semantics and query processing. We investigate various semantics of this type of queries. To process such queries, a straightforward approach is to simply glue the techniques developed for ranking-only and clustering-only together. This approach is costly since both ranking and clustering are treated as blocking post-processing tasks upon Boolean query results by existing techniques. We propose a summary-based evaluation method that utilizes bitmap index to seamlessly integrate Boolean conditions, clustering, and ranking. Experimental study shows that our approach significantly outperforms the straightforward one and maintains high clustering quality.
2266285
Relaxing join and selection queries
Database users can be frustrated by having an empty answer to a query. In this paper, we propose a framework to systematically relax queries involving joins and selections. When considering relaxing a query condition, intuitively one seeks the 'minimal' amount of relaxation that yields an answer. We first characterize the types of answers that we return to relaxed queries. We then propose a lattice based framework in order to aid query relaxation. Nodes in the lattice correspond to different ways to relax queries. We characterize the properties of relaxation at each node and present algorithms to compute the corresponding answer. We then discuss how to traverse this lattice in a way that a non-empty query answer is obtained with the minimum amount of query condition relaxation. We implemented this framework and we present our results of a thorough performance evaluation using real and synthetic data. Our results indicate the practical utility of our framework.