Content-based image retrieval using a signature graph and a self-organizing map

Thanh The Van; Thanh Manh Le

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 2, page 423-438
  • ISSN: 1641-876X

Abstract

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In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.

How to cite

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Thanh The Van, and Thanh Manh Le. "Content-based image retrieval using a signature graph and a self-organizing map." International Journal of Applied Mathematics and Computer Science 26.2 (2016): 423-438. <http://eudml.org/doc/280122>.

@article{ThanhTheVan2016,
abstract = {In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.},
author = {Thanh The Van, Thanh Manh Le},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {binary signature; similarity measure; signature graph; image retrieval},
language = {eng},
number = {2},
pages = {423-438},
title = {Content-based image retrieval using a signature graph and a self-organizing map},
url = {http://eudml.org/doc/280122},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Thanh The Van
AU - Thanh Manh Le
TI - Content-based image retrieval using a signature graph and a self-organizing map
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 2
SP - 423
EP - 438
AB - In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.
LA - eng
KW - binary signature; similarity measure; signature graph; image retrieval
UR - http://eudml.org/doc/280122
ER -

References

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