Summary and Scope:

Dictionary Learning (DL) is a long-standing popular topic for visual image representation due to its great success to image restoration, de-noising and classification, etc. DL aims at representing data using a linear combination of a few highly correlated atoms in a dictionary D. But how to obtain a desired dictionary from inputs still remains a challenging task to date. It is noteworthy that most existing DL algorithms represent data using a single-layer framework, so they usually fail to obtain the deep feature representations with more useful and valuable hidden information discovered. In recent years, with the fast development of deep learning and multi-layer neural networks, it will be helpful to propose deeper or multi-layer DL frameworks for representation learning. Although certain efforts have been made to incorporate the deep learning into the DL, most designs of so-called deep dictionary learning (DDL) algorithms are still less straightforward. For example, some existing algorithms feed deep features of the deep networks into DL for representation learning, or perform the DL first and then use the reconstructed data for deep learning. As such, it is now necessary to integrate the DL with deep networks, and explore the advanced algorithms, theories and optimization approaches for the deep dictionary learning.

In this special issue, we invite contributions from diverse research fields, such as deep representation learning, image processing, and computer vision, etc., developing novel algorithms from high-dimensional data.

The topics of interest include, but are not limited to:

Survey/vision/review of dictionary learning

Robust dictionary learning

Online dictionary learning

Deep/multi-layer dictionary learning

Convolutional dictionary learning

Structured dictionary learning

Bayesian dictionary learning

Coupled/semi-coupled dictionary learning

Optimization for dictionary learning/deep dictionary learning

Applications to image processing



Submission Guideline

Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of Neurocomputing at http://www.journals.elsevier.com/neurocomputing/. All the papers will be peer reviewed following the Neurocomputing reviewing procedures.

Important Dates:

Paper submission due: April 15, 2020

First notification: July 15, 2020

Revision: Oct 15, 2020

Final decision made on all manuscripts: Nov 15, 2020

Lead Guest Editor:

Porf. Zhao Zhang, Hefei University of Technology, China

Guest Editors:

Dr. Sheng Li, University of Georgia, USA Dr. Zheng Zhang, University of Queensland, Australia Prof. Meng Wang, Hefei University of Technology, China
Biographies of Guest Editors:

Zhao Zhang (SM’17- ) is a Full Professor at the School of Computer Science & School of Artificial Intelligence, Hefei University of Technology, Hefei, China. He received the Ph.D. degree from the Department of Electronic Engineering (EE) at City University of Hong Kong, supervised by Prof. Tommy W.S. Chow (IEEE Fellow), in 2013. Dr. Zhang was a Visiting Research Engineer at the National University of Singapore, working with Prof. Shuicheng Yan (IEEE Fellow and IAPR Fellow), from Feb to May 2012. He then visited the National Laboratory of Pattern Recognition (NLPR) at Chinese Academy of Sciences (CAS), working with Prof. Cheng-Lin Liu (IEEE Fellow and IAPR Fellow), from Sep to Dec 2012. During Oct 2013 to Oct 2018, he was a Distinguished Associate Professor at the School of Computer Science and Technology, Soochow University, Suzhou, China. His current research interests include Multimedia Data Mining & Machine Learning, Image Processing & Computer Vision. He has authored/co-authored over 80 technical papers published at prestigious journals and conferences, such as IEEE TIP (4), IEEE TKDE (5), IEEE TNNLS (4), IEEE TSP, IEEE TCSVT, IEEE TCYB, IEEE TBD, IEEE TII (2), ACM TIST, Pattern Recognition (6), Neural Networks (8), Computer Vision and Image Understanding, Neurocomputing (3), IJCAI, ACM Multimedia, ICDM (4), ICASSP and ICMR, etc. Specifically, he has published 17 regular papers in IEEE/ACM Transactions journals as the first-author or corresponding author. Dr. Zhang is serving/served as an Associate Editor (AE) for Neurocomputing, IEEE Access and IET Image Processing. Besides, he has been acting as a Senior Program Committee (SPC) member or Area Chair (AC) of PAKDD、BMVC、ICTAI, and a PC member for 10+ popular prestigious conferences (e.g., CVPR、ICCV、IJCAI、AAAI、ACM MM、ICDM、CIKM and SDM). He is now a Senior Member of the IEEE. (Personal homepage: http://www.escience.cn/people/cszzhang/index.html)

Sheng Li (SM’19-) received the Ph.D. degree from Northeastern University, Boston, MA, in 2017. He is currently a Tenure-Track Assistant Professor in the Department of Computer Science at University of Georgia. From 2017-2018, heworked as a Data Scientist at the Big Data Experience Lab, Adobe Research, San Jose, CA. He has published over 50 papers at leading conferences and journals. He received the best paper awards (or nominations) at SDM 2014, IEEE ICME 2014, and IEEE FG 2013. He serves on the Editorial Board of Neural Computing and Applications, Neurcomputing, and serves as an Associate Editor of IET Image Processing and SPIE Journal of Electronic Imaging. He has also served as a reviewer for several IEEE Transactions, and program committee member for NIPS, IJCAI, AAAI, and KDD. His research interests include robust machine learning, dictionary learning, visual intelligence, and behavior modeling. He is now a Senior Member of the IEEE.

Zheng Zhang received the M.S and Ph.D. degree from Harbin Institute of Technology (Shenzhen) in 2014 and 2018, respectively. Currently, he is a Postdoctoral Research Fellow in the University of Queensland. He has authored or co-authored over 20 technical papers published at prestigious international journals and conferences, including the IEEE TPAMI, IEEE TNNLS, IEEE TIP, PR and ECCV. He received the Best Paper Award from 2014 International Conference on Smart Computing. His current research interests include machine learning and computer vision.

Meng Wang is a Professor in the Hefei University of Technology, China. He received the B.E. degree and Ph.D. degree in the Special Class for the Gifted Young and signal and information processing from the University of Science and Technology of China (USTC), Hefei, China, respectively. He previously worked as an associate researcher at Microsoft Research Asia, and then a core member in a startup in Bay area. After that, he worked as a senior research fellow in National University of Singapore. His current research interests include multimedia content analysis, search, mining, recommendation, and large-scale computing. He has authored 6 book chapters and over 100 journal and conference papers in these areas, including TMM, TNNLS, TCSVT, TIP, TOMCCAP, ACM MM, WWW, SIGIR, ICDM, etc. He received the paper awards from ACM MM 2009 (Best Paper Award), ACM MM 2010 (Best Paper Award), MMM 2010 (Best Paper Award), ICIMCS 2012 (Best Paper Award), ACM MM 2012 (Best Demo Award), ICDM 2014 (Best Student Paper Award), PCM 2015 (Best Paper Award), SIGIR 2015 (Best Paper Honorable Mention), IEEE TMM 2015 (Best Paper Honorable Mention), and IEEE TMM 2016 (Best Paper Honorable Mention). He is the recipient of ACM SIGMM Rising Star Award 2014. He is/has been an Associate Editor of IEEE Trans. on Knowledge and Data Engineering (TKDE), IEEE Trans. on Neural Networks and Learning Systems (TNNLS) and IEEE Trans. on Circuits and Systems for Video Technology (TCSVT).