<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

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

Faculty of Information Technology University of Sciences/Hue University, 77 Nguyen Hue Street, Hue City, Vietnam

Center for Information Technology HCMC University of Food Industry, 140 Le Trong Tan Street, Tan Phu District, HoChiMinh City, Vietnam

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Content-based image retrieval using a signature graph and a self-organizing map"}

In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied...

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

Published Online: Jul 02, 2016

Page range: 423 - 438

Received: Jul 11, 2015

Accepted: Mar 02, 2016

DOI: https://doi.org/10.1515/amcs-2016-0030

Keywords
binary signature, similarity measure, signature graph, image retrieval

© by Thanh The Van

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Published Online: Jul 02, 2016

Page range: 423 - 438

Received: Jul 11, 2015

Accepted: Mar 02, 2016

DOI: https://doi.org/10.1515/amcs-2016-0030

Keywordsbinary signature, similarity measure, signature graph, image retrieval

© by Thanh The Van

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
binary signature, similarity measure, signature graph, image retrieval