Koji Kotani

A fast full search equivalent encoding method for vector quantization by using appropriate features

The encoding process of vector quantization (VQ) is very heavy and it constrains VQs application a great deal. In order to speed up VQ encoding, it is most important to avoid unnecessary Euclidean distance computation (k-D) as much as possible by the difference check that uses simpler features (low dimensional) while winner searching is going on. Sum (1-D) and partial sums (2-D) are used together as the appropriate features in this paper because they are the first 2 simplest features. Then, sum difference and partial sum difference are computed as the estimations of Euclidean distance and they are connected to each other by the Cauchy-Schwarz inequality so as to reject a lot of codewords. For typical standard images with very different details (Lena, F-16, Pepper and Baboon), the final must-do Euclidean distance computation using the proposed method can be reduced to less than 10% as compared to full search (FS) meanwhile keeping the PSNR not degraded.

A FAST LOCAL SEARCH ALGORITHM USING HISTOGRAM FEATURES FOR DNA SEQUENCE DATABASE

DNA sequence search is a very important topic in bioinformatics algorithm development. However, this task usually spends much computational time to search on large DNA sequence database. In this paper, we propose an efficient hierarchical DNA sequence search algorithm to improve the search speed while the accuracy is being kept constant. For a given query DNA sequence, firstly, a fast local search algorithm using histogram features is used as a filtering mechanism before scanning the sequences in the database. An overlapping processing is newly added to improve the robustness of the algorithm. A large number of DNA sequences with low similarity will be excluded for latter searching. The Smith-Waterman algorithm is then applied to each remainder sequences. Experimental results using GenBank se- quence data show the proposed algorithm combining histogram information and Smith-Waterman algorithm is more efficient for DNA se- quence search. Keywords- Fast search, DNA sequence, Histogram feature, Smith-Waterman algorithm, Local search.