Number Of Matching Operations Research Articles

Enhanced Levenshtein Edit Distance Method functioning as a String-to-String Similarity Measure

Levenshtein is a Minimum Edit Distance method; it is usually used in spell checking applications for generatingcandidates. The method computes the number of the required edit operations to transform one string to another and it canrecognize three types of edit operations: deletion, insertion, and substitution of one letter. Damerau modified the Levenshteinmethod to consider another type of edit operations, the transposition of two adjacent letters, in addition to theconsidered three types. However, the modification suffers from the time complexity which was added to the original quadratictime complexity of the original method. In this paper, we proposed a modification for the original Levenshtein toconsider the same four types using very small number of matching operations which resulted in a shorter execution timeand a similarity measure is also achieved to exploit the resulted distance from any Edit Distance method for finding the amountof similarity between two given strings.

An Adaptive Algorithm for Improving the Fractal Image Compression (FIC)

In this paper an adaptive algorithm is proposed to reduce the long time that has been taken in the Fractal Image Compression (FIC) technique. This algorithm worked on reducing the number of matching operations between range and domain blocks by reducing both of the range and domain blocks needed in the matching process, for this purpose, two techniques have been proposed; the first one is called Range Exclusion ( RE ), in this technique variance factor is used to reduce the number of range blocks by excluding ranges of the homogenous or flat regions from the matching process; the second technique is called Reducing the Domain Image Size ( RDIZ ), it is responsible for the reduction of the domain by minimizing the Domain Image Size to 1/16 th instead of 1/4 th of the original image size used in the traditional FIC. This in turn will affect the encoding time, compression ratio and the reconstructed image quality. For getting best results, the two techniques are coupled in one algorithm; the new algorithm is called ( RD-RE ). The tested (256x256) gray images are partitioned into fixed (4x4) blocks and then compressed using visual C++ 6.0 code. The results show that RE technique is faster and gets more compression ratio than the traditional FIC and keeping a high reconstructed images quality while RD-RE is faster and it gets higher compression ratio than RE but with slight loss in the reconstructed image quality.

Fast visual search using focused color matching?active search

There exist methods of searching for an object whose position and size are not known in an image. To obtain an accurate result by such methods, the focused region of an input image has to be compared many times with its reference image by varying its position and size. This makes fast searching difficult. This paper describes a method of fast active search for an object and its position in an image by using the color histogram, which is stable to variations of shape of the object. When the input image of an object is compared with its reference image, the upper limit of the similarity in the image region is calculated from the input image in a certain position using the algebraic characteristics of the color histogram. If the upper limit is smaller than the search value, no search in this region is necessary, so that the number of matching operations can be greatly reduced. This method involves no approximations and has a computation speed about 10 to 1000 times faster than conventional exhaustive search methods. Applications of the proposed method, such as tracking, retrieval, and counting, are also described. © 2000 Scripta Technica, Syst Comp Jpn, 31(9): 81–88, 2000