Complexity

Complex Deep Learning and Evolutionary Computing Models in Computer Vision


Publishing date
01 Apr 2019
Status
Published
Submission deadline
16 Nov 2018

Lead Editor

1Northumbria University, Newcastle, UK

2Deakin University, Geelong, Australia

3Lancaster University, Lancaster, UK


Complex Deep Learning and Evolutionary Computing Models in Computer Vision

Description

Describing the content of a video/image automatically using natural language is a challenging task, which is significantly harder than image labelling and objects recognition. It requires not only accurate recognition of objects and background scenes but also their attributes, relations, and saliency (i.e., the likelihood of a recognized object to be mentioned in the generated text). Deep learning models have demonstrated great success in dealing with such complex computer vision tasks. Examples include the use of deep convolutional neural networks combined with recurrent models for image caption generation. Nevertheless, such deep learning approaches tend to lose details pertaining to important regional aspects in the image, and the generated captions tend to be short and less informative. As a result, complex deep learning models that are able to capture and translate regional details for better people/object/scene classification to facilitate accurate image label description generation are required. On the other hand, the success of deep learning models also relies on the identification of optimal architectures and hyperparameters that fit the task. In this regard, the superior search capabilities of evolutionary computing algorithms allow them to tackle diverse optimization problems including identification of optimal architectures and hyperparameters of deep learning models.

This special issue is dedicated to mathematical modelling, simulation, and/or analysis of deep learning and evolutionary computing models with complex, adaptive behaviours, and phenomena in science and in real life, as well as application and implementation of such complex deep learning and evolutionary computing models to computer vision tasks. The aim is to stimulate studies pertaining to not only complex deep learning-based computer vision systems but also optimal topology and hyperparameter identification for such deep complex networks through evolutionary computing and related paradigms.

Potential topics include but are not limited to the following:

  • Complex deep neural networks on image description generation
  • Complex deep neural networks on visual question generation or answering
  • Deep neural networks and complex modelling on image segmentation
  • Deep neural networks and complex modelling on visual saliency detection
  • Deep neural networks and complex modelling on human or object attribute prediction
  • Deep neural networks and complex modelling on large-scale object recognition
  • Deep neural networks and complex modelling on scene classification
  • Deep neural networks and complex modelling on human action recognition
  • Deep neural networks and complex modelling on age estimation
  • Deep neural networks and complex modelling on facial and bodily expression recognition
  • Deep neural networks and complex modelling on language generation and speech recognition
  • Evolutionary computing techniques for optimal structure identification for diverse deep complex neural networks and modelling
  • Evolutionary computing techniques for optimal hyperparameter selection for diverse deep complex neural networks and modelling
  • Evolutionary computing techniques for optimal topology and hyperparameter identification for diverse complex ensemble neural networks and modelling
  • Complex neural networks for health monitoring and surveillance
  • Deep learning applications and complex system modelling for social media data analysis (e.g., Facebook photo description generation, online news/medical image annotation, script generation for movies, automatic description generation for historical photos/paintings in museums, and health/security surveillance)

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 1671340
  • - Editorial

Complex Deep Learning and Evolutionary Computing Models in Computer Vision

Li Zhang | Chee Peng Lim | Jungong Han
  • Special Issue
  • - Volume 2019
  • - Article ID 3581419
  • - Research Article

Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition

Ijaz Ul Haq | Amin Ullah | ... | Sung Wook Baik
  • Special Issue
  • - Volume 2019
  • - Article ID 5498606
  • - Research Article

Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications

Fangyuan Lei | Jun Cai | ... | Huimin Zhao
  • Special Issue
  • - Volume 2019
  • - Article ID 8641074
  • - Research Article

A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading

Xiaoming Wang | Xinbo Zhao | Jinchang Ren
  • Special Issue
  • - Volume 2019
  • - Article ID 9180391
  • - Research Article

Weakly Supervised Deep Semantic Segmentation Using CNN and ELM with Semantic Candidate Regions

Xinying Xu | Guiqing Li | ... | Xinlin Xie
  • Special Issue
  • - Volume 2019
  • - Article ID 8176489
  • - Research Article

Passive Initialization Method Based on Motion Characteristics for Monocular SLAM

Yu Yang | Jing Xiong | ... | Jie Li
  • Special Issue
  • - Volume 2019
  • - Article ID 3563674
  • - Research Article

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Yonghong Yu | Li Zhang | ... | Jing Jiang
  • Special Issue
  • - Volume 2019
  • - Article ID 9345861
  • - Research Article

Focal CTC Loss for Chinese Optical Character Recognition on Unbalanced Datasets

Xinjie Feng | Hongxun Yao | Shengping Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 5676095
  • - Research Article

Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking

Ming-xin Jiang | Chao Deng | ... | Haiyan Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 5298294
  • - Research Article

Design and Implementation of an Assistive Real-Time Red Lionfish Detection System for AUV/ROVs

M-Mahdi Naddaf-Sh | Harley Myler | Hassan Zargarzadeh
Complexity
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 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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