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18 results on '"Marzouki, Kirmene"'

1. Neuro-Glial Interaction: SONG-Net

Author

Marzouki, Kirmene, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Arik, Sabri, editor, Huang, Tingwen, editor, Lai, Weng Kin, editor, and Liu, Qingshan, editor

Published
2015
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2. Modeling Astrocyte-Neuron Interactions

Author

Ben Chikha, Soukeina, Marzouki, Kirmene, Ben Ahmed, Samir, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Arik, Sabri, editor, Huang, Tingwen, editor, Lai, Weng Kin, editor, and Liu, Qingshan, editor

Published
2015
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3. Optimization of BOW Using Self Organizing Map Artificial Neural Network in Similar Images Retrieval Systems

Author

Elleuch, Zied, Marzouki, Kirmene, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sanches, João M., editor, Micó, Luisa, editor, and Cardoso, Jaime S., editor

Published
2013
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4. Making Standard SOM Invariant to the Initial Conditions

Author

Chikha, Soukeina Ben, Marzouki, Kirmene, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cabestany, Joan, editor, Sandoval, Francisco, editor, Prieto, Alberto, editor, and Corchado, Juan M., editor

Published
2009
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12. Multi-index structure based on SIFT and color features for large scale image retrieval.

Author

Elleuch, Zied and Marzouki, Kirmene

Subjects
IMAGE retrieval, BAG-of-words model (Computer science), CONTENT-based image retrieval, SIGNAL quantization, FLICKER fusion
Abstract

During the past few years, the Bag-of-Words (BoW) model based on SIFT features has been one of the most adopted approaches by the Content-Based Image Retrieval (CBIR) systems. However, these CBIR systems have shown some weaknesses and shortcomings especially for large scale image collections. This is due to two main causes: First, information is lost in the quantization step and second, the SIFT features describe only the local gradient. To tackle these issues, we proposed to take advantage of the Hamming Embedding, soft assignment and multiple assignment techniques, on the one hand, and to fuse SIFT and color features at the indexing level in a multi-index structure, on the other. In fact, in this paper, generic and non-parametric image retrieval schemes as well as a novel multi-IDF design based on multi-index structure were proposed. Extensive experiments were conducted on three public datasets (Holidays, Ukbench and MIR Flickr 1 M as distractor). The experimental results are promising and outperform the state-of-the-art CBIR systems. In addition, only 117 bits are needed to represent each key-point which enables us to make our image retrieval schema suitable for large-scale experiments. [ABSTRACT FROM AUTHOR]

Published
2017
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17. Novel Learning Algorithm Aiming at Generating a Unique Units Distribution in Standard SOM.

Author

Ribeiro, Bernardete, Albrecht, Rudolf F., Dobnikar, Andrej, Pearson, David W., Steele, Nigel C., Marzouki, Kirmene, and Yamakawa, Takeshi

Subjects
SELF-organizing maps, ARTIFICIAL neural networks, ALGORITHMS, SELF-organizing systems, NEURAL circuitry
Abstract

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, one of the limitations of Self Organizing Maps algorithm, is that every SOM is different and finds different similarities among the sample vectors each time the initial conditions are changed. In this paper, we propose a modification of the SOM basic algorithm in order to make the resulted mapping invariant to the initial conditions. We extend the neighborhood concept to processing units, selected in a fashionable manner, other than those commonly selected relatively to the immediate surroundings of the best matching unit. We also introduce a new learning function for the newly introduced neighbors. The modified algorithm was tested on a color classification application and performed very well in comparison with the traditional SOM. [ABSTRACT FROM AUTHOR]

Published
2005
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