Near-duplicate image recognition based on the rank distribution of the brightness clusters cardinality

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5 Citations (Scopus)

Abstract

In this paper the usage of multi-step segmentation for near-duplicate image recognition is investigated. The clustering of image pixels brightness is used for segmentation. The clustering is realized by means of a recurrent neural network. The search pattern based on the rank distributions of the brightness clusters cardinality is suggested. Experimental results on the near-duplicate image recognition based on the application of the suggested search pattern are given. It is shown that the use of a multi-step segmentation and rank distributions of the brightness clusters cardinality allows one to successfully recognize the duplicates, which are received by a considerable visual distortion of the original image or by the change of image scale.

Original languageEnglish
Pages (from-to)811-817
Number of pages7
JournalComputer Optics
Volume38
Issue number4
Publication statusPublished - 2014

Fingerprint

Image recognition
Luminance
brightness
Recurrent neural networks
Pixels
pixels

Keywords

  • Clustering
  • Image
  • Image recognition
  • Pixel
  • Point mapping
  • Ranking distribution
  • Recurrent neural network
  • Segmentation

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Computer Science Applications

Cite this

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abstract = "In this paper the usage of multi-step segmentation for near-duplicate image recognition is investigated. The clustering of image pixels brightness is used for segmentation. The clustering is realized by means of a recurrent neural network. The search pattern based on the rank distributions of the brightness clusters cardinality is suggested. Experimental results on the near-duplicate image recognition based on the application of the suggested search pattern are given. It is shown that the use of a multi-step segmentation and rank distributions of the brightness clusters cardinality allows one to successfully recognize the duplicates, which are received by a considerable visual distortion of the original image or by the change of image scale.",
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AU - Nemirovskiy, V. B.

AU - Stoyanov, A. K.

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N2 - In this paper the usage of multi-step segmentation for near-duplicate image recognition is investigated. The clustering of image pixels brightness is used for segmentation. The clustering is realized by means of a recurrent neural network. The search pattern based on the rank distributions of the brightness clusters cardinality is suggested. Experimental results on the near-duplicate image recognition based on the application of the suggested search pattern are given. It is shown that the use of a multi-step segmentation and rank distributions of the brightness clusters cardinality allows one to successfully recognize the duplicates, which are received by a considerable visual distortion of the original image or by the change of image scale.

AB - In this paper the usage of multi-step segmentation for near-duplicate image recognition is investigated. The clustering of image pixels brightness is used for segmentation. The clustering is realized by means of a recurrent neural network. The search pattern based on the rank distributions of the brightness clusters cardinality is suggested. Experimental results on the near-duplicate image recognition based on the application of the suggested search pattern are given. It is shown that the use of a multi-step segmentation and rank distributions of the brightness clusters cardinality allows one to successfully recognize the duplicates, which are received by a considerable visual distortion of the original image or by the change of image scale.

KW - Clustering

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KW - Image recognition

KW - Pixel

KW - Point mapping

KW - Ranking distribution

KW - Recurrent neural network

KW - Segmentation

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