Image Mining by Data Compactness and Manifold Learning

Author

Song, Yuqing ; Li, Yaohui

Author_Institution

Sch. of Automotive & Transp., Tianjin Univ. of Technol. & Educ., Tianjin, China

fYear

2012

fDate

1-3 Nov. 2012

Firstpage

29

Lastpage

32

Abstract

One important issue in image mining is how to analyze the compactness of image data and apply it to image mining. In this paper we study the class compactness and boundary compactness of image data, which are used in image classification and data confining. The data confining results in relevance graph, which is used in calculating the distances between images. Manifold learning techniques are applied in the computation of distances between images and manifolds of images. Image retrieval is based on these distances. Experiments are reported to show the effectiveness of our approach.

Keywords

data compression; data mining; graph theory; image classification; image coding; image retrieval; learning (artificial intelligence); relevance feedback; data confinement; image classification; image data boundary compactness analysis; image data class compactness analysis; image mining; image retrieval; manifold learning techniques; relevance graph; Association rules; Educational institutions; Image classification; Image retrieval; Manifolds; Training; boundary compactness; class compactness; image classification; image mining; image retrieval; manifold learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Networks and Intelligent Systems (ICINIS), 2012 Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4673-3083-1

Type

conf

DOI

10.1109/ICINIS.2012.53

Filename

6376477