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Special issue on selected and extended papers from the 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015)
JID: NEUCOM
ARTICLE IN PRESS
[m5G;June 13, 2017;19:17]
Neurocomputing 0 0 0 (2017) 1–2
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
Special issue on selected and extended papers from the 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015)
The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015) aims at a collective venue for introducing world frontier researchers to China and for introducing researchers of an ever developing and huge population of Chinese colleagues to international communities. This conference has a broad scope, including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics and computational biology, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning. Experimental results for contributions in established areas are encouraged to use the largest and most challenging existing publicly available datasets. In the following, we present a synopsis of papers collected in this special issue, which address some aspects of the above research problems. More specifically, some papers study challenging issues, such as sparse representation, dictionary learning, transfer learning and unsupervised learning, in machine learning and pattern recognition, while others target at solving real-world problems, such as image classification/categorization, motion detection, medical diagnosis, web ranking, natural language modeling, human pose estimation, image inpainting, video quality assessment and so on. 1. Papers in this special issue The goals of this special issue are to collect distinguished research papers from IScIDE 2015 and provide the reader with an overview of the state of the art in this field. All of the papers were rigorously reviewed and improved in accordance with the journal policy. This special issue consists of 12 papers. The contents of these papers range from various machine learning methods and techniques to application areas such as computer vision, social media, natural language processing, etc. The first three papers are related to sparse representation and dictionary learning, with applications to motion detection, image classification and categorization. Liu et al. propose a double sparse representation method with a dynamic dictionary updating process for abnormal crowd motion detection. Each test sample is judged separately by two sparse representation classifiers. Fuzzy integral is also employed in the detection process. Yang et al. propose a novel model of Fisher discrimination dictionary pair learning, in http://dx.doi.org/10.1016/j.neucom.2017.05.077 0925-2312/© 2017 Elsevier B.V. All rights reserved.
which Fisher discrimination information is embedded into analysis representation, analysis dictionary, and synthesis dictionary representation. A classifier based on this method has been successfully applied to various image classification problems. Zhang et al. propose a non-linear non-negative sparse representation model for image categorization. In the dictionary learning stage, the proposed model extends the kernel SVD by embedding the non-negative sparse coding. In the sparse coding stage, a nonlinear update rule is proposed to obtain the sparse matrix. Transfer learning and unsupervised learning are important research problems in the machine learning community. Yang et al. propose a co-transfer clustering method to deal with heterogeneous data from different domains. The proposed method consists of two steps, learning the subspace of different domains while uncovering the latent common topics and preserving intrinsic geometric structure, followed by simultaneous clustering in all domains via symmetric nonnegative matrix factorization. Combining traditional statistics and machine learning is a promising way to enhance the performance of some specific learning algorithms. This issue includes two papers in this direction, with respect to medical diagnosis and analysis. Jiang et al. apply the integrative hypothesis test that combined both hypothesis test and classifier to identifying miRNAs for differentiation between lung cancer and Chronic Obstructive Pulmonary Disease. As the most commonly used statistical classification models are less capable of handling the intensity non-uniformity and partial volume effect, Li et al. propose a novel approach to improve brain voxel classification in MRI images by considering all effects simultaneously. Two selected papers deal with structural data such as web data and natural languages. Du et al. study how to assign a score to each webpages, just as the PageRank does. Based on previous works, the authors build a Path Trust Knowledge Graph model to assign priority values to unvisited web pages. Language model is a fundamental tool for many applications in natural language processing. Most language models trained on large datasets are difficult to be adapted to other domains which have a small dataset available. Guo et al. present novel language models based on tunable discounting mechanisms. The language models are trained on a large dataset, but their discounting parameters can be tuned to a target dataset afterwards.
JID: NEUCOM 2
ARTICLE IN PRESS
[m5G;June 13, 2017;19:17]
Editorial / Neurocomputing 000 (2017) 1–2
Other papers in this issue are related to various applications in intelligence science and data engineering, including 3D human pose estimation, image inpainting, image/video defogging and video quality assessment. Guo et al. present a promising method through dimension reduction based on sparse spectral embedding to fuse multiple features. An ensemble of nearest neighbor regression in low-rank multi-view feature space is then adopted to infer 3D human poses from monocular videos. Wang et al. extend the well-known exemplar-based inpainting method through a space varying update strategy and a matching confidence term. Liu et al. present a large size single image computation acceleration bilateral filtering based defogging scheme, and further introduce a simplified filtering alternative and the corresponding FPGA defogging architecture for large size video real time defogging on smart TV. Jia et al. present a novel VQA model by exploring and exploiting the compact representation of energy in the three-dimensional discrete cosine transform (3D-DCT) domain. Acknowledgment The guest editors would like to thank the authors who submitted their work. They thank the reviewers who spent their valuable time in reviewing the manuscripts. They would also thank Vera Kamphuis, for providing useful suggestions and excellent support during the development and processing of the special issue.
Guest Editors
Shiguang Shan∗ Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China Deng Cai College of Computer Science, Zhejiang University, Hangzhou, Zhejiang Province, China Cheng Deng School of Electronic Engineering, Xidian University, Xi’an 710071, Shaanxi Province, China Hong Chang Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China ∗ Corresponding author E-mail address: [email protected] (S. Shan)
Received 21 May 2017 Accepted 23 May 2017
ARTICLE IN PRESS
[m5G;June 13, 2017;19:17]
Neurocomputing 0 0 0 (2017) 1–2
Contents lists available at ScienceDirect
Neurocomputing journal homepage: www.elsevier.com/locate/neucom
Editorial
Special issue on selected and extended papers from the 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015)
The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015) aims at a collective venue for introducing world frontier researchers to China and for introducing researchers of an ever developing and huge population of Chinese colleagues to international communities. This conference has a broad scope, including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics and computational biology, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning. Experimental results for contributions in established areas are encouraged to use the largest and most challenging existing publicly available datasets. In the following, we present a synopsis of papers collected in this special issue, which address some aspects of the above research problems. More specifically, some papers study challenging issues, such as sparse representation, dictionary learning, transfer learning and unsupervised learning, in machine learning and pattern recognition, while others target at solving real-world problems, such as image classification/categorization, motion detection, medical diagnosis, web ranking, natural language modeling, human pose estimation, image inpainting, video quality assessment and so on. 1. Papers in this special issue The goals of this special issue are to collect distinguished research papers from IScIDE 2015 and provide the reader with an overview of the state of the art in this field. All of the papers were rigorously reviewed and improved in accordance with the journal policy. This special issue consists of 12 papers. The contents of these papers range from various machine learning methods and techniques to application areas such as computer vision, social media, natural language processing, etc. The first three papers are related to sparse representation and dictionary learning, with applications to motion detection, image classification and categorization. Liu et al. propose a double sparse representation method with a dynamic dictionary updating process for abnormal crowd motion detection. Each test sample is judged separately by two sparse representation classifiers. Fuzzy integral is also employed in the detection process. Yang et al. propose a novel model of Fisher discrimination dictionary pair learning, in http://dx.doi.org/10.1016/j.neucom.2017.05.077 0925-2312/© 2017 Elsevier B.V. All rights reserved.
which Fisher discrimination information is embedded into analysis representation, analysis dictionary, and synthesis dictionary representation. A classifier based on this method has been successfully applied to various image classification problems. Zhang et al. propose a non-linear non-negative sparse representation model for image categorization. In the dictionary learning stage, the proposed model extends the kernel SVD by embedding the non-negative sparse coding. In the sparse coding stage, a nonlinear update rule is proposed to obtain the sparse matrix. Transfer learning and unsupervised learning are important research problems in the machine learning community. Yang et al. propose a co-transfer clustering method to deal with heterogeneous data from different domains. The proposed method consists of two steps, learning the subspace of different domains while uncovering the latent common topics and preserving intrinsic geometric structure, followed by simultaneous clustering in all domains via symmetric nonnegative matrix factorization. Combining traditional statistics and machine learning is a promising way to enhance the performance of some specific learning algorithms. This issue includes two papers in this direction, with respect to medical diagnosis and analysis. Jiang et al. apply the integrative hypothesis test that combined both hypothesis test and classifier to identifying miRNAs for differentiation between lung cancer and Chronic Obstructive Pulmonary Disease. As the most commonly used statistical classification models are less capable of handling the intensity non-uniformity and partial volume effect, Li et al. propose a novel approach to improve brain voxel classification in MRI images by considering all effects simultaneously. Two selected papers deal with structural data such as web data and natural languages. Du et al. study how to assign a score to each webpages, just as the PageRank does. Based on previous works, the authors build a Path Trust Knowledge Graph model to assign priority values to unvisited web pages. Language model is a fundamental tool for many applications in natural language processing. Most language models trained on large datasets are difficult to be adapted to other domains which have a small dataset available. Guo et al. present novel language models based on tunable discounting mechanisms. The language models are trained on a large dataset, but their discounting parameters can be tuned to a target dataset afterwards.
JID: NEUCOM 2
ARTICLE IN PRESS
[m5G;June 13, 2017;19:17]
Editorial / Neurocomputing 000 (2017) 1–2
Other papers in this issue are related to various applications in intelligence science and data engineering, including 3D human pose estimation, image inpainting, image/video defogging and video quality assessment. Guo et al. present a promising method through dimension reduction based on sparse spectral embedding to fuse multiple features. An ensemble of nearest neighbor regression in low-rank multi-view feature space is then adopted to infer 3D human poses from monocular videos. Wang et al. extend the well-known exemplar-based inpainting method through a space varying update strategy and a matching confidence term. Liu et al. present a large size single image computation acceleration bilateral filtering based defogging scheme, and further introduce a simplified filtering alternative and the corresponding FPGA defogging architecture for large size video real time defogging on smart TV. Jia et al. present a novel VQA model by exploring and exploiting the compact representation of energy in the three-dimensional discrete cosine transform (3D-DCT) domain. Acknowledgment The guest editors would like to thank the authors who submitted their work. They thank the reviewers who spent their valuable time in reviewing the manuscripts. They would also thank Vera Kamphuis, for providing useful suggestions and excellent support during the development and processing of the special issue.
Guest Editors
Shiguang Shan∗ Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China Deng Cai College of Computer Science, Zhejiang University, Hangzhou, Zhejiang Province, China Cheng Deng School of Electronic Engineering, Xidian University, Xi’an 710071, Shaanxi Province, China Hong Chang Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China ∗ Corresponding author E-mail address: [email protected] (S. Shan)
Received 21 May 2017 Accepted 23 May 2017