Measuring Source Code Similarity Using Reference Vectors

Ohno, Asako; Murao, Hajime

doi:10.1109/icicic.2006.308

Cited by 3 publications

(1 citation statement)

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“…In our previous study, we have developed a similarity measuring system for program source codes and confirmed that it had retrieved almost all relevant source codes for a query source code through our experiments [4]. Then we accomplished its core algorithm as a new feature extraction algorithm for digital data called FRef.…”

Section: Introductionmentioning

confidence: 91%

A Similarity Measuring Method for Images Based on the Feature Extraction Algorithm using Reference Vectors

Ohno

Murao²

2007

Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007)

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We propose a similarity measuring method for images based on our feature extraction algorithm. The method represents features of an image as a feature vector called reference vector which is a relative measure extracted indirectly from images while many of existing methods use an absolute similarity measure extracted directly from images. A reference vector is calculated from correlation matrices of an image and reference images. Considering reference images as axes of a coordination system, our method enabled users to extract their intended features by selecting appropriate images as reference images. This significant characteristic of the method is effective to measure similarity based on users' preference and to differentiate an image from others. In this paper, we illustrate our method in detail and demonstrate its effectiveness through experiments.

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Section: Introductionmentioning

confidence: 91%