A simple computational model for image retrieval with weighted multifeatures based on orthogonal polynomials and genetic algorithm

Krishnamoorthi, R.; Devi, S. Sathiya

doi:10.1016/j.neucom.2012.05.030

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…Using mechanisms related to gene transfer in sexual reproduction, the procedure produces a new population of potential solutions (Goldberg, 2002;Marwala, 2010;Xhafa et al, 2013;Smolka, 2013). Lin et al (2013) successfully applied GA to solve a multi-objective simulation optimization problem in surgical services whereas Krishnamoorthi and Sathiya Devi (2013) successfully applied GA for image retrieval. Robles-Ortega et al (2013) successfully applied GA to produce textured urban models whereas Zamani (2013) successfully applied GA for resource-constrained scheduling problem.…”

Section: Genetic Algorithmmentioning

confidence: 99%

What is a Correlation Machine?

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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This chapter describes what a correlation machine is as well as the types of correlations machines. Correlations machines considered in this chapter are the auto-associative memory machine trained using genetic algorithm (GA), Expectation Maximization (EM) trained Gaussian mixed models and the principal component analysis (PCA) machine. These methods were used to model the antenatal HIV database. From this chapter, auto-associative memory machine is chosen as a correlation machine to be considered for the rest of this book.

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Section: Genetic Algorithmmentioning

confidence: 99%

What is a Correlation Machine?

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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“…However, it showed that single image feature was far from retrieving the satisfied results [16,17]. Multi-feature fusion method therefore based on image content itself gets the place of single feature gradually in image retrieving [18,19]. Efficient features extraction methods are essential in the process of obtaining multi-feature information contained in image itself.…”

Section: Introductionmentioning

confidence: 99%

“…Krishnamoorthi etc. proposed a image retrieval method with weighted multifeature set based on multiresolution enhanced orthogonal polynomials model and genetic algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning

Xia

Zhang

et al. 2017

Neurocomputing

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A method for automatic image annotation based on multi-feature fusion and multi-label learning algorithm was proposed in this paper. In the process of feature fusion, rotation-invariant uniform local binary pattern histogram distribution and counting of connected regions in image were extracted and utilized fully. Besides traditional n-order color moments and texture information, rotation-invariant uniform LBP histogram distribution, connected regions number, weighted histogram's integral were appended to image features which aided to improve the average precision. Based on multi-label learning k-nearest neighbor algorithm and Corel5k image data set, comparisons among different dimensional features combinations were made to show that the proposed method outperformed that of traditional one with only basic color moments and texture distribution. The average precision was showed to be improved from 0.2898 to 0.3954 in automatic image annotation in our experimental results.

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Shape Based Image Classification and Retrieval with Multiresolution Enhanced Orthogonal Polynomial Model

Devi

Krishnamoorthi

2013

Advances in Intelligent Systems and Computing

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A simple computational model for image retrieval with weighted multifeatures based on orthogonal polynomials and genetic algorithm

Cited by 8 publications

References 49 publications

What is a Correlation Machine?

What is a Correlation Machine?

Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning

Shape Based Image Classification and Retrieval with Multiresolution Enhanced Orthogonal Polynomial Model

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