Multilayer Neural Networks and Nearest Neighbor Classifier Performances for Image Annotation

Oujaoura, Mustapha; Minaoui, Brahim; Fakir, Mohamed

doi:10.14569/ijacsa.2012.031126

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…They can be combined together with descriptors in order to increase the recognition rates of amazigh characters. This document presents the combination of some classification approaches (Neuronal Network, Bayesian network, SVM, nearest neighbor classifiers) known in the recognition field [7].…”

Section: Classificationmentioning

confidence: 99%

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Recognition of Isolated Printed Tifinagh Characters

Oujaoura¹,

Minaoui²,

Fakir³

et al. 2014

IJCA

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Most of the reported works in the field of character recognition systems achieve modest results by using a single method for calculating the parameters of the character image and a single approach in the classification phase of the system. So, in order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed Tifinagh characters by using a fusion of some classifiers and a combination of some features extraction methods. The Legendre moments, Zernike moments, Hu moments, Walsh transform, GIST and texture are used as descriptors in the features extraction phase due to their invariance to translation, rotation and scaling changes. In the classification phase, the neural network, the Bayesian network, the multiclass SVM (Support Vector Machine) and the nearest neighbour classifiers are combined together. The experimental results of each single features extraction method with each single classification method are compared with our approach to show its robustness. A recognition rate of 100 % is achieved by using some combined descriptors and classifiers.

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Section: Classificationmentioning

confidence: 99%

“…Fig. 3 shows an example of a three layer network used in this paper, having input layer formed by M nodes, one hidden layer formed by L nodes, and output layer formed by N nodes [7].…”

Section: Neural Networkmentioning

confidence: 99%

Recognition of Isolated Printed Tifinagh Characters

Oujaoura¹,

Minaoui²,

Fakir³

et al. 2014

IJCA

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“…As an improvement of the previous works [12], [13], [14], [15], we consider this problem for image that are only described globally or not yet labelled with a suitable text terms. Also, individual classifier and features descriptor results are limited or not suited for some situations [16].…”

Section: Annotation Systemmentioning

confidence: 99%

Combined descriptors and classifiers for automatic image annotation

Oujaoura

Minaoui

Fakir

2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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Due to the large amounts of multimedia data prevalent on the Web, researchers and industries are beginning to pay more attention to the Multimedia Semantic Web. Despite of decades of research, neither model based approaches can provide quality annotation to images. Many features extraction method and classifiers are used singly, with modest results, for automatic image annotation. The proposed approach is to select and combine together some efficient descriptors and classifiers. This document provides a semantic annotation system that combines some descriptors and classifiers in order to increase the accuracy of the annotation system. The color histograms, Texture, GIST and invariant moments, used as features extraction methods, are combined together with multiclass support vector machine, Bayesian networks, Neural networks and nearest neighbour classifiers, in order to annotate the image content with the appropriate keywords. The accuracy of the proposed approach is supported by the good experimental results obtained from ETH-80 databases.

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“…As a result, this document presents some approaches (Neuronal Network, SVM, nearest neighbor classifiers) known in the recognition field [4].…”

Section: Classificationmentioning

confidence: 99%

Invariant Descriptors and Classifiers Combination for Recognition of Isolated Printed Tifinagh Characters

Oujaoura¹,

Ayachi²,

Minaoui³

et al. 2013

SpecialIssue

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Abstract-In order to improve the recognition rate, this document proposes an automatic system to recognize isolated printed Tifinagh characters by using a fusion of 3 classifiers and a combination of some features extraction methods. The Legendre moments, Zernike moments and Hu moments are used as descriptors in the features extraction phase due to their invariance to translation, rotation and scaling changes. In the classification phase, the neural network, the multiclass SVM (Support Vector Machine) and the nearest neighbour classifiers are combined together. The experimental results of each single features extraction method and each single classification method are compared with our approach to show its robustness.

show abstract

Multilayer Neural Networks and Nearest Neighbor Classifier Performances for Image Annotation

Cited by 8 publications

References 15 publications

Recognition of Isolated Printed Tifinagh Characters

Recognition of Isolated Printed Tifinagh Characters

Combined descriptors and classifiers for automatic image annotation

Invariant Descriptors and Classifiers Combination for Recognition of Isolated Printed Tifinagh Characters

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