1269749
Complexity-adaptive search algorithm for block motion estimation
We propose a complexity-adaptive fast block matching algorithm design strategy that allows the user to terminate the algorithm at any target computational complexity. Two complexity-adaptive techniques, frame level complexity allocation and block level complexity allocation, are developed to approach the global complexity-distortion optimization A buffer control strategy is proposed to dynamically adjust the target complexity of one frame. The predictive complexity-distortion benefit list (PCDB list) technique is employed to allocate the target complexity into each block. By these techniques, we modify the full-search block matching, three-step search, new three-step search, and four-step search to the complexity-adaptive algorithms. Experimental results show that the complexity-adaptive algorithms could achieve better performance than traditional fixed algorithms from the viewpoint of complexity-distoftion optimization.
28811627
Recognizing depth-first-search trees in parallel
Consider that T. is a given spanning tree of an undirected graph G which contains a vertices and m (/spl ges/n-1) edges. In this paper, we propose an O(m/p+log m)-time parallel algorithm using p processors on the EREW PRAM model to determine whether T is a depth-first-search tree of G. Our algorithm is optimal in time complexity and speed-up.<<ETX>>
31774377
Factors in graphs with odd-cycle property
Abstract We present some conditions for the existence of a ( g , f )-factor or a ( g , f )-parity factor in a graph G with the odd-cycle property that any two odd cycles of G either have a vertex in common or are joined by an edge.
11808205
Mending broken handwriting with a macrostructure analysis method to improve recognition
Abstract Broken characters always create problems in handwriting recognition systems, especially those using boundary and/or skeleton information. This paper presents a macrostructure analysis (MSA) mending method based on skeleton and boundary information and an MSA that investigates the stroke tending direction and other properties of handwritings. A new skeleton end extension algorithm is introduced, which compensates the defectiveness of the skeletonization algorithm and obtains a satisfactory skeleton. When combined with suitable parameters, improved performance from a handwriting classifier is achieved. The experimental results from over 13 000 numerals show the efficiency and robustness of the proposed method, raising recognition rates by over 10% for broken handwritten digits, from 74.8% to 86.4%.
14858727
BIO-AJAX: an extensible framework for biological data cleaning
As databases become more pervasive through the biological sciences, various data quality issues regarding data legacy, data uniformity and data duplication arise. Due to the nature of this data, each of these problems is non-trivial. For biological data to be corrected and standardized, new methods and frameworks must be developed. This paper proposes one such framework, called BIO-AJAX, which uses principles from data cleaning to improve data quality in biological information systems, specifically in TreeBASE.
12107864
Kernel Trick Embedded Gaussian Mixture Model
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance.
28415004
Scientific Data Mining: A Case Study
Scientific data mining is the activity of finding significant information in scientific data. This paper presents an example of scientific data mining: the discovery of approximately common patterns in RNA secondary structures. We represent an RNA secondary structure by an ordered labeled tree based on a previously proposed scheme. The patterns in the trees are substructures that can differ in both substitutions and deletions/insertions of nodes of the trees. Our techniques incorporate approximate tree matching algorithms and novel heuristics for discovery and optimization. Experimental results obtained by running these algorithms on both generated data and RNA secondary structures show the good performance of the algorithms. It is shown that the optimization heuristics speed up the discovery algorithm by a factor of 10. Moreover, our optimized approach is 100,000 times faster than the brute force method.