Color Reduction Using the Combination of the Kohonen Self-Organized Feature Map and the Gustafson-Kessel Fuzzy Algorithm

Zagoris, Konstantinos; Papamarkos, Nikos; Koustoudis, Ioannis

doi:10.1007/978-3-540-73499-4_53

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…The GNG algorithm was chosen for test comparisons because it has been the SOM with variable topology, most used for color quantization according to the literature [1,22]. Analogously, the FS-SOM was chosen as the most used [2,20] frequency sensitive SOM model.…”

Section: Comparing Som Fs-som Gng and Larfsommentioning

confidence: 99%

“…Some color quantization implementations using neural network architectures were proposed in [1,2,18,20,22]. Chang et al [2] proposed a modified SOM called a Frequency Sensitive Self-organizing Map (FS-SOM).…”

Section: Introductionmentioning

confidence: 99%

“…LARFSOM is self-adaptive with respect to the number of final colors, a suitable feature for color segmentation, as opposed to both the fixed color quantization models [2,4,18,20,22] and also to the color segmentation model [17] with an initial fixed-size map. LARFSOM does not use supervised learning as [5] does, thus incremental learning is viable.…”

Section: Introductionmentioning

confidence: 99%

“…The SA-SOM, which shares many characteristics with the FS-SOM, achieved higher PSNR rates than FS-SOM and required many fewer training samples, resulting in a faster training time. In [22], a hybrid neuro-fuzzy technique based on a two stage process is proposed by Zagoris et al Initially a SOM produces a predefined number of image colors and then the Gustafson-Kessel Fuzzy Classifier (GKFC) is used to reduce the final number of colors.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Local adaptive receptive field self-organizing map for image color segmentation

Araújo

Costa

2009

Image and Vision Computing

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Section: Comparing Som Fs-som Gng and Larfsommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Local adaptive receptive field self-organizing map for image color segmentation

Araújo

Costa

2009

Image and Vision Computing

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“…The Gustafson-kessel is an extension of the fuzzy c-means algorithm [22]. It used mahalanobis distance.…”

Section: Gustafson-kessel(gk) Algorithmmentioning

confidence: 99%

Using Artificial Intelligence Techniques For Intrusion Detection System

Ahmed¹,

Khaleel²

2013

AL-Rafidain Journal of Computer Sciences and Mathematics

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Along with the development and growth of the internet network, and the rapid expansion of World Wide Web and local network systems have changed the computing world in the last decade. Nowadays, as more people make use of the internet, their computers and the valuable data in their computer system contain become more exposed to attackers. Therefore, there is an increasing need to protect computer and network from attacks and unauthorized access. Such that network intrusion classification and detection systems to prevent unlawful accesses. This work has taken the advantage of classification and detection abilities of Artificial Intelligent Techniques AITs algorithms to recognize intrusion(attack) and also detect new attacks. These algorithms are used to multi classifier and binary classifier for network intrusion and detect it, AITs such as unsupervised and supervised fuzzy clustering algorithms ( Fuzzy C-Mean FCM, Gustafson-Kessel GK, and Possibilistic C-Means PCM ), was applied to classify intrusion into 23 classes according to the subtype of attack. The same dataset classifies it into 5 classes according to the type of attacks (Normal, DoS, Probe, U2R, R2L). And also classifies this dataset into 2 classes (Normal, and Attack), one for normal traffic and another for attack, also these algorithms are used to detect intrusion.Other techniques were used which are artificial neural network (ANN) represented by counter propagation neural network (CPN) which is hybrid learning (supervised and unsupervised) that is applied to classify intrusion into 23, 5 and 2 class(es) and used it to detect the network intrusions, and then we combined fuzzy c-mean with two layers Kohonen layer and Grossberg layer for counter propagation neural network to produce the proposed approach or system that called it fuzzy counter propagation neural network (FCPN) were applied it to classify network intrusion into 23, 5 and 2 class(es) and detect the intrusion. DARPA 1999 (Defense Advanced Research Project Agency) dataset which is represented by Knowledge Discovery and Data mining (KDD) cup 99 dataset was used for both training and testing. This research evaluates the performance of the approaches that are used that obtained high classification and detection rate with low false alarm rate. The performance of the proposed approach FCPN is the best if it is compared with the other approaches that are used and with previous works. Finally, in this research comparisons are made between the results obtained from the application of these algorithms on this dataset and the FCPN is the best approach that is implemented into Laptop where, CPU 2.27GH and RAM are 2.00 GB.

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An improved SOM algorithm and its application to color feature extraction

Chen

Liu

Huang

et al. 2013

Neural Comput & Applic

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Reducing the redundancy of dominant color features in an image and meanwhile preserving the diversity and quality of extracted colors is of importance in many applications such as image analysis and compression. This paper presents an improved self-organization map (SOM) algorithm namely MFD-SOM and its application to color feature extraction from images. Different from the winner-take-all competitive principle held by conventional SOM algorithms, MFD-SOM prevents, to a certain degree, features of non-principal components in the training data from being weakened or lost in the learning process, which is conductive to preserving the diversity of extracted features. Besides, MFD-SOM adopts a new way to update weight vectors of neurons, which helps to reduce the redundancy in features extracted from the principal components. In addition, we apply a linear neighborhood function in the proposed algorithm aiming to improve its performance on color feature extraction. Experimental results of feature extraction on artificial datasets and benchmark image datasets demonstrate the characteristics of the MFD-SOM algorithm.

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Color Reduction Using the Combination of the Kohonen Self-Organized Feature Map and the Gustafson-Kessel Fuzzy Algorithm

Cited by 7 publications

References 19 publications

Local adaptive receptive field self-organizing map for image color segmentation

Local adaptive receptive field self-organizing map for image color segmentation

Using Artificial Intelligence Techniques For Intrusion Detection System

An improved SOM algorithm and its application to color feature extraction

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