BibTeX
@MISC{_anapplication,
author = {},
title = {An Application of LSI and M-tree in Image Retrieval},
year = {}
}
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Abstract
Abstract. When dealing with image databases, we often need to solve the problem of how to retrieve a desired set of images effectively and efficiently. As a representation of images, there are commonly used some high-dimensional vectors of extracted features, since in such a way the content-based image retrieval is turned into a geometric-search problem. In this article we present a case study of feature extraction from raw image data by means of the LSI method (singular-value decomposition, respectively). Simultaneously, we show how such a kind of feature extraction can be used for efficient and effective similarity retrieval using the M-tree index. Because of the application to image retrieval, we also show some interesting effects of LSI, which are not directly obvious in the area of text retrieval (where LSI came from). LSI, similarity search in image databases, M-tree 1
Keyphrases
image retrieval image database feature extraction effective similarity retrieval interesting effect desired set lsi method case study similarity search extracted feature geometric-search problem text retrieval raw image data singular-value decomposition content-based image retrieval high-dimensional vector m-tree index
