A model for the analysis of neighbor finding in pointer-based quadtrees

A natural byproduct of the tree-like nature of the quadtree is that many basic image processing operations can be implemented as tree traversals which differ in the nature of the computation that is performed at each node. Some of these computations involve the inspection of a node's adjacent neighbors (termed neighbor finding). A new model is developed for images represented by quadtrees, and it is used to analyze various neighbor-finding techniques. The model's predicted costs for neighbor finding correlate very closely with empirical results and it is superior to the model that was used previously.     

Hash key-based image encryption using crossover operator and chaos

This paper proposes a color image encryption scheme using one-time keys based on crossover operator, chaos and the Secure Hash Algorithm(SHA-2). The (SHA-2) is employed to generate a 256-bit hash value from both the plain-image and the secret hash keys to make the key stream change in each encryption process. The SHA-2 value is employed to generate three initial values of the chaotic system. The permutation-diffusion process is based on the crossover operator and XOR operator, respectively. Experimental results and security analysis show that the scheme can achieve good encryption result through only one round encryption process, the key space is large enough to resist against common attacks,so the scheme is reliable to be applied in image encryption and secure communication.     

Evidential data mining: precise support and confidence

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.     

Efficient Regular Data Structures and Algorithms for Dilation, Location, and Proximity Problems

In this paper we investigate data structures obtained by a recursive partitioning of the multi- dimensional input domain into regions of equal size. One of the best known examples of such a structure is the quadtree . It is used here as a basis for more complex data structures. We also provide multidimensional versions of the stratified tree by van Emde Boas [vEB]. We show that under the assumption that the input points have limited precision (i.e., are drawn from the integer grid of size u ) these data structures yield efficient solutions to many important problems. In particular, they allow us to achieve O(log log u) time per operation for dynamic approximate nearest neighbor (under insertions and deletions) and exact on-line closest pair (under insertions only) in any constant number of dimensions. They allow O(log log u) point location in a given planar shape or in its expansion (dilation by a ball of a given radius). Finally, we provide a linear time (optimal) algorithm for computing the expansion of a shape represented by a region quadtree. This result shows that the spatial order imposed by this regular data structure is sufficient to optimize the operation of dilation by a ball. Received January 19, 1999; revised November 4, 1999.     

Computing geometric properties of images represented by linear quadtrees

The region quadtree is a hierarchical data structure that finds use in applications such as image processing, computer graphics, pattern recognition, robotics, and cartography. In order to save space, a number of pointerless quadtree representations (termed linear quadtrees) have been proposed. One representation maintains the nodes in a list ordered according to a preorder traversal of the quadtree. Using such an image representation and a graph definition of a quadtree, a general algorithm to compute geometric image properties such as the perimeter, the Euler number, and the connected components of an image is developed and analyzed. The algorithm differs from the conventional approaches to images represented by quadtrees in that it does not make use of neighbor finding methods that require the location of a nearest common ancestor. Instead, it makes use of a staircase-like data structure to represent the blocks that have been already processed. The worst-case execution time of the algorithm, when used to compute the perimeter, is proportional to the number of leaf nodes in the quadtree, which is optimal. For an image of size 2n × 2n, the perimeter algorithm requires only four arrays of 2n positions each for working storage. This makes it well suited to processing linear quadtrees residing in secondary storage. Implementation experience has confirmed its superiority to existing approaches to computing geometric properties for images represented by quadtrees.     

Positive and negative association rule mining on XML data streams in database as a service concept

In recent years, data mining has become one of the most popular techniques for data owners to determine their strategies. Association rule mining is a data mining approach that is used widely in traditional databases and usually to find the positive association rules. However, there are some other challenging rule mining topics like data stream mining and negative association rule mining. Besides, organizations want to concentrate on their own business and outsource the rest of their work. This approach is named “database as a service concept” and provides lots of benefits to data owner, but, at the same time, brings out some security problems. In this paper, a rule mining system has been proposed that provides efficient and secure solution to positive and negative association rule computation on XML data streams in database as a service concept. The system is implemented and several experiments have been done with different synthetic data sets to show the performance and efficiency of the proposed system.