Earticle

초록

영어

This paper presents a new nonlinear approximate indexing method for high-dimensional data such as multimedia data. The new indexing method is designed for approximate similarity searches and all the work is performed in the transformed Gaussian space. In this indexing method, we first map the input space into a feature space via the Gaussian mapping, and then compute the top eigenvectors in the Gaussian space to capture the cluster structure based on the eigenvectors. We describe each cluster with a minimal hypersphere containing all objects in the cluster, derive the similarity measure for each cluster individually and construct a bitmap index for each cluster. Finally we transform the nearest neighbor query into the hyper-rectangular range query and search the clusters near the query point. The experimental results for our new indexing method show considerable effectiveness and efficiency.

목차

Abstract
 1. Introduction
 2. Nonlinear Similarity Model
 3. Clustering
 4. Local Similarity Measure
 5. Cluster Description
 6. Similarity Query Model
 7. Similarity Bitmap Indexing Method
  7.1. Index Creation
  7.2. Similarity Search
 8. Performance Experiments
 9. Conclusions
 References

키워드

저자

• Guang-Ho Cha [ Seoul National University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605