Rotation and scale invariant antinoise PCNN features for content-based image retrieval

Title:

Rotation and scale invariant antinoise PCNN features for content-based image retrieval

Creator:

Zhang, Jiuwen, Zhan, Kun, and Ma, Yide

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:42281209-0203-499f-b637-b76c7ec5a907
uuid:42281209-0203-499f-b637-b76c7ec5a907

Subject:

Content-based image retrieval, pulse-coupled neural network, rotation and scale invariance, and entropy sequence

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Content-based image retrieval (CBIR) of images, especially those with different orientation, scale changes and noise affects, are a challenging and important problem in the image analysis. This paper proposes an effective scheme for rotation and scale invariant antinoise retrieval using pulse-coupled neural network (PCNN) features. The PCNN generates series of pulse images, which are binary and represent different features of the original image. The series of pulse images can be then calculated to an entropy sequence called the feature of the image. The experimental results show that the retrieval scheme is effective in extracting rotation and scale invariant features and it also performs better robustness to noise.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:2

Harvested from:

CDK

Metadata only:

false