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A brief introduction to the special issue for ISNN2010
Neurocomputing 76 (2012) 1
Contents lists available at SciVerse ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
A brief introduction to the special issue for ISNN2010
This special issue of Neurocomputing presents 6 original articles that are extended versions of selected papers from the Seventh International Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6–9, 2010. ISNN2010 received 591 submissions from more than 40 countries and regions, providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses on neural networks, and applications in diverse areas. The selected six papers cover from visual neural coding, selective attention to brain consciousness. All these papers were thoroughly reviewed once more by at least two independent experts. The first paper, entitled Force Field Convergence Map and LogGabor Filter Based Multi-view Ear Feature Extraction, proposes a novel multi-view ear feature extraction approach using the force field convergence map and log-Gabor filter. Experimental results suggest that the proposed convergence map with log-Gabor filter is advantageous in multi-view ear classification. The second paper, entitled An Approach for Visual Attention Based on Biquaternion and Its Application for Ship Detection in Multispectral Imagery, proposes a biquaternion-based approach for visual attention. The method represents high dimensional data in the form of biquaternion and uses the phase spectrum of biquaternion Fourier transform to generate a required saliency map. Experimental results on real multispectral remote sensing data show that the proposed method has excellent performance in ship detection and is robust against white noise. The third paper, entitled Gender Classification by Combining Clothing, Hair and Facial Component Classifiers, proposes a novel gender classification using both local facial features and external information, such as hair and clothing, to achieve higher robustness for occlusions and noise. The paper introduces various strategies, such as fuzzy integral, maximal, sum, voting, and product rule, to integrate the outputs of classifiers of different features. Experimental results demonstrated that the proposed model improves classification accuracy, even when images contain occlusions, noise, and illumination changes. The fourth paper, entitled A Hierarchical Latent Topic Model Based on Sparse Coding, develops a new hierarchical latent topic model based on sparse coding. The words represented in this model are continuous, rather than discrete as in the previous topic models. It assumes that the distribution of continuous words is governed by the Laplacian distribution and the words are generated by topics, which are latent variables in this model. This model is a generalization of the traditional Latent Dirichlet Allocation by introducing the concept-continuous words. Experimental results
on natural scene categorization and object classification show that the method is a valuable direction to generalize topic models. The fifth paper, entitled Modelling of Brain Consciousness based on Collaborative Adaptive Filters, proposes a new method for the discrimination between discrete states of brain consciousness using nonlinear features within the electroencephalogram (EEG). A collaborative adaptive filtering architecture, using a convex combination of adaptive filters, is implemented. Simulations based upon a number of different filter combinations illustrate the suitability of this approach to differentiate between the coma and quasi-braindeath states based upon fundamental signal characteristics. The sixth paper, entitled Causality Analysis of Neural Connectivity: New Tool and Limitations of Spectral Granger Causality (GC), proposes a causality definition for the linear regression model, which is related to coefficients of the model, power spectra of the signals and the noise terms, in order to avoid the inherent limitations of spectral GC. Simulations demonstrate that the results of spectral GC analysis are misleading but the results from the proposed definition are reasonable. The guest editors would like to thank all authors for their contributions and all reviewers for their excellent work. We would also like to thank the Neurocomputing editorial board for providing us great support in publishing ISNN2010 papers as a special issue. We would like to express our sincere gratitude to Profs. Jun Wang and Baoliang Lu, the general chairs of ISNN2010, for organizing the excellent symposium.
Guest Editors Liqing Zhang n Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai, China E-mail address: [email protected] James Kwok The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong E-mail address: [email protected] Changshui Zhang Department of Automation, Tsinghua University, Beijing 100084, China E-mail address: [email protected] 10 August 2011
n
0925-2312/$ - see front matter & 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.neucom.2011.08.012
Tel./fax: þ 86 21 34204423.
Contents lists available at SciVerse ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
A brief introduction to the special issue for ISNN2010
This special issue of Neurocomputing presents 6 original articles that are extended versions of selected papers from the Seventh International Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6–9, 2010. ISNN2010 received 591 submissions from more than 40 countries and regions, providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses on neural networks, and applications in diverse areas. The selected six papers cover from visual neural coding, selective attention to brain consciousness. All these papers were thoroughly reviewed once more by at least two independent experts. The first paper, entitled Force Field Convergence Map and LogGabor Filter Based Multi-view Ear Feature Extraction, proposes a novel multi-view ear feature extraction approach using the force field convergence map and log-Gabor filter. Experimental results suggest that the proposed convergence map with log-Gabor filter is advantageous in multi-view ear classification. The second paper, entitled An Approach for Visual Attention Based on Biquaternion and Its Application for Ship Detection in Multispectral Imagery, proposes a biquaternion-based approach for visual attention. The method represents high dimensional data in the form of biquaternion and uses the phase spectrum of biquaternion Fourier transform to generate a required saliency map. Experimental results on real multispectral remote sensing data show that the proposed method has excellent performance in ship detection and is robust against white noise. The third paper, entitled Gender Classification by Combining Clothing, Hair and Facial Component Classifiers, proposes a novel gender classification using both local facial features and external information, such as hair and clothing, to achieve higher robustness for occlusions and noise. The paper introduces various strategies, such as fuzzy integral, maximal, sum, voting, and product rule, to integrate the outputs of classifiers of different features. Experimental results demonstrated that the proposed model improves classification accuracy, even when images contain occlusions, noise, and illumination changes. The fourth paper, entitled A Hierarchical Latent Topic Model Based on Sparse Coding, develops a new hierarchical latent topic model based on sparse coding. The words represented in this model are continuous, rather than discrete as in the previous topic models. It assumes that the distribution of continuous words is governed by the Laplacian distribution and the words are generated by topics, which are latent variables in this model. This model is a generalization of the traditional Latent Dirichlet Allocation by introducing the concept-continuous words. Experimental results
on natural scene categorization and object classification show that the method is a valuable direction to generalize topic models. The fifth paper, entitled Modelling of Brain Consciousness based on Collaborative Adaptive Filters, proposes a new method for the discrimination between discrete states of brain consciousness using nonlinear features within the electroencephalogram (EEG). A collaborative adaptive filtering architecture, using a convex combination of adaptive filters, is implemented. Simulations based upon a number of different filter combinations illustrate the suitability of this approach to differentiate between the coma and quasi-braindeath states based upon fundamental signal characteristics. The sixth paper, entitled Causality Analysis of Neural Connectivity: New Tool and Limitations of Spectral Granger Causality (GC), proposes a causality definition for the linear regression model, which is related to coefficients of the model, power spectra of the signals and the noise terms, in order to avoid the inherent limitations of spectral GC. Simulations demonstrate that the results of spectral GC analysis are misleading but the results from the proposed definition are reasonable. The guest editors would like to thank all authors for their contributions and all reviewers for their excellent work. We would also like to thank the Neurocomputing editorial board for providing us great support in publishing ISNN2010 papers as a special issue. We would like to express our sincere gratitude to Profs. Jun Wang and Baoliang Lu, the general chairs of ISNN2010, for organizing the excellent symposium.
Guest Editors Liqing Zhang n Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai, China E-mail address: [email protected] James Kwok The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong E-mail address: [email protected] Changshui Zhang Department of Automation, Tsinghua University, Beijing 100084, China E-mail address: [email protected] 10 August 2011
n
0925-2312/$ - see front matter & 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.neucom.2011.08.012
Tel./fax: þ 86 21 34204423.