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Shenglan Liu - IEEE Xplore Author Profile

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Author details

Shenglan Liu

Also published under: Shenlan Liu, Sheng-Lan Liu

Publications

28

Citations

100

Publications by Year

20152024

Co-Authors:

Shuyuan ChenCheng ChengSuhao DingYu-Ning DingYifeng Dong

Show All Co-Authors (66)

Shenglan Liu

Also published under: Shenlan Liu, Sheng-Lan Liu

Affiliation

School of Computer Science and Technology

Dalian University of Technology

Dalian, China

Publication Topics

Action Recognition,
Graph Convolutional Network,
Temporal Convolutional Network,
Action Segmentation,
Convolutional Neural Network,
Receptive Field,
Temporal Action Segmentation,
Video Frames,
Action Classes,
Action Recognition Task,
Convolution Operation,
Convolutional Layers

Biography

Shenglan Liu (Member, IEEE) received the Ph.D. degree from the School of Computer Science and Technology, Dalian University of Technology, Dalian, China.,He is currently an Associate Professor with the School of Innovation and Entrepreneurship, Dalian University of Technology. He has published articles in the IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, and so on. His current research interests include pattern recognition, image retrieval, and machinelearning.(Based on document published on 17 April 2020).

Publications

28

Citations

100

Publications by Year

20152024

Co-Authors:

Shuyuan Chen
Cheng Cheng
Suhao Ding
Yu-Ning Ding
Yifeng Dong

Show All Co-Authors (66)

Author's Published Works

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Showing 1-25 of 28 results

Conferences (16)

Journals (11)

Early Access Articles (1)

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Shenglan Liu(25)
Lin Feng(14)
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School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China(6)
Dalian University of Technology, Dalian, China(5)
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China(4)
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China(4)
School of Computer Science and Technology, Dalian University of Technology, Dalian, China(3)
2016 12th World Congress on Intelligent Control and Automation (WCICA)(6)
IEEE Transactions on Neural Networks and Learning Systems(5)
IEEE Access(3)
2019 IEEE International Conference on Smart Internet of Things (SmartIoT)(2)
2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)(2)
IEEE(28)
Guilin, China(6)
Fuzhou, China(2)
Tianjin, China(2)
Dalian, China(1)
Madurai, India(1)
Action Recognition(5)
Convolutional Neural Network(5)
Graph Convolutional Network(5)
Image Retrieval(5)
Temporal Convolutional Network(5)

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“Where Does the Devil Lie?”: Multimodal Multitask Collaborative Revision Network for Trusted Road Segmentation

Guoguang Hua;Dalian Zheng;Shishun Tian;Wenbin Zou;Shenglan Liu;Xia Li

IEEE Transactions on Multimedia

Year: 2024 | Volume: 26 | Journal Article |

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Road segmentation is an essential component of navigation systems. Although recent advancements in road segmentation, the occurrence of failure segmentations remains inevitable. For safety-critical tasks, e.g., navigation, knowing when and where road segmentation fails is crucial. In this paper, we propose a novel trusted road segmentation architecture, namely Multimodal Multitask Collaborative Re...Show More

“Where Does the Devil Lie?”: Multimodal Multitask Collaborative Revision Network for Trusted Road Segmentation

Guoguang Hua;Dalian Zheng;Shishun Tian;Wenbin Zou;Shenglan Liu;Xia Li

IEEE Transactions on Multimedia

Year: 2024 | Volume: 26 | Journal Article |

Multidimensional Refinement Graph Convolutional Network With Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition

Sheng-Lan Liu;Yu-Ning Ding;Jin-Rong Zhang;Kai-Yuan Liu;Si-Fan Zhang;Fei-Long Wang;Gao Huang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Early Access Article |

Cited by: Papers (1)

Graph convolutional networks (GCNs) have been widely used in skeleton-based action recognition. However, existing approaches are limited in fine-grained action recognition due to the similarity of interclass data. Moreover, the noisy data from pose extraction increase the challenge of fine-grained recognition. In this work, we propose a flexible attention block called channel-variable spatial–temp...Show More

Multidimensional Refinement Graph Convolutional Network With Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition

Sheng-Lan Liu;Yu-Ning Ding;Jin-Rong Zhang;Kai-Yuan Liu;Si-Fan Zhang;Fei-Long Wang;Gao Huang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Early Access Article |

Involving Distinguished Temporal Graph Convolutional Networks for Skeleton-Based Temporal Action Segmentation

Yun-Heng Li;Kai-Yuan Liu;Sheng-Lan Liu;Lin Feng;Hong Qiao

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 1 | Journal Article |

Cited by: Papers (5)

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For RGB-based temporal action segmentation (TAS), excellent methods that capture frame-level features have achieved remarkable performance. However, for motion-centered TAS, it is still challenging for existing methods that ignore the extraction of spatial features of joints. In addition, inaccurate action boundaries caused by the frames of similar motion destroy the integrity of the action segmen...Show More

Involving Distinguished Temporal Graph Convolutional Networks for Skeleton-Based Temporal Action Segmentation

Yun-Heng Li;Kai-Yuan Liu;Sheng-Lan Liu;Lin Feng;Hong Qiao

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2024 | Volume: 34, Issue: 1 | Journal Article |

Hierarchical Neighbors Embedding

Shenglan Liu;Yang Yu;Kaiyuan Liu;Feilong Wang;Wujun Wen;Hong Qiao

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 6 | Journal Article |

Cited by: Papers (2)

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Manifold learning now plays an important role in machine learning and many relevant applications. In spite of the superior performance of manifold learning techniques in dealing with nonlinear data distribution, their performance would drop when facing the problem of data sparsity. It is hard to obtain satisfactory embeddings when sparsely sampled high-dimensional data are mapped into the observat...Show More

Hierarchical Neighbors Embedding

Shenglan Liu;Yang Yu;Kaiyuan Liu;Feilong Wang;Wujun Wen;Hong Qiao

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 6 | Journal Article |

Multi-Scale Spatio-Temporal Aggregation Network for Human Motion Prediction

Haoyu Su;Shenglan Liu;Zewen Gao;Yifeng Dong;Junshi Yang;Suhao Ding

2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Year: 2023 | Conference Paper |

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Human motion prediction is a fundamental problem in computer vision, aimed at predicting future motion sequence from historical motion sequence. Some recent works have shown that Graph Convolutional Networks(GCNs) perform well in modeling the correlation between human joints, and Temporal Convolutional Networks(TCNs) have been widely recognized for solving sequence problems. However, the locality ...Show More

Multi-Scale Spatio-Temporal Aggregation Network for Human Motion Prediction

Haoyu Su;Shenglan Liu;Zewen Gao;Yifeng Dong;Junshi Yang;Suhao Ding

2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Year: 2023 | Conference Paper |

Streaming Video Temporal Action Segmentation in Real Time

Wujun Wen;Yunheng Li;Zhuben Dong;Lin Feng;Wanxiao Yang;Shenlan Liu

2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Temporal action segmentation (TAS) is a critical step toward long-term video understanding. Recent studies based on the pre-extracted video features instead of raw video image in-formation. However, those models are trained complicatedly and limit application scenarios. It is hard for them to segment human actions of video in real time because they must work after the full video features are extra...Show More

Streaming Video Temporal Action Segmentation in Real Time

Wujun Wen;Yunheng Li;Zhuben Dong;Lin Feng;Wanxiao Yang;Shenlan Liu

2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Year: 2023 | Conference Paper |

SS-INR: Spatial-Spectral Implicit Neural Representation Network for Hyperspectral and Multispectral Image Fusion

Xinying Wang;Cheng Cheng;Shenglan Liu;Ruoxi Song;Xianghai Wang;Lin Feng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (5)

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Due to the limitation of imaging equipment, it is difficult to acquire hyperspectral images (HSIs) with high spatial resolution directly. Existing approaches improve the resolution of HSIs by fusing multispectral image (MSI) and HSI. However, most of them are only feed-forward. They only learn low-resolution (LR) to high-resolution (HR) feature mappings without considering the ill-posedness of sup...Show More

SS-INR: Spatial-Spectral Implicit Neural Representation Network for Hyperspectral and Multispectral Image Fusion

Xinying Wang;Cheng Cheng;Shenglan Liu;Ruoxi Song;Xianghai Wang;Lin Feng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Reducing the Label Bias for Timestamp Supervised Temporal Action Segmentation

Kaiyuan Liu;Yunheng Li;Shenglan Liu;Chenwei Tan;Zihang Shao

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Cited by: Papers (3)

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Timestamp supervised temporal action segmentation (TSTAS) is more cost-effective than fully supervised counterparts. However, previous approaches suffer from severe label bias due to over-reliance on sparse timestamp annotations, resulting in unsatisfactory performance. In this paper, we propose the Debiasing-TSTAS (D-TSTAS) framework by exploiting unannotated frames to alleviate this bias from tw...Show More

Reducing the Label Bias for Timestamp Supervised Temporal Action Segmentation

Kaiyuan Liu;Yunheng Li;Shenglan Liu;Chenwei Tan;Zihang Shao

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2023 | Conference Paper |

Image Classification Based on a Weighted CNN Model

Hao Peng;Jian Zhou;Shenglan Liu

2022 World Automation Congress (WAC)

Year: 2022 | Conference Paper |

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This paper explores a deeper network architecture diagram, which can be viewed as an enhanced version of AlexNet. In the design of the winder, a smaller winder core is used, which verifies that the small size of the winder core in the deep network can not only reduce the parameters, but also achieve better results. The concept of Inception was introduced and the idea of "widening" was designed und...Show More

Image Classification Based on a Weighted CNN Model

Hao Peng;Jian Zhou;Shenglan Liu

2022 World Automation Congress (WAC)

Year: 2022 | Conference Paper |

DF-Net: Dynamic and Folding Network for 3D Point Cloud Completion

Yao Xiao;Yang Li;Qingjun Yu;Shenglan Liu;Jialin Gang

Year: 2022 | Volume: 10 | Journal Article |

Cited by: Papers (2)

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The development of 3D sensors encourages researchers to process point cloud data directly. Point cloud data requires less memory but conveys more detailed 3D shape information. However, because of occlusion, sensing distance and other reasons, sensors usually cannot get a complete 3D shape. In this paper, we propose a Dynamic and Folding Network (DF-Net) to address the precise point cloud completi...Show More

DF-Net: Dynamic and Folding Network for 3D Point Cloud Completion

Yao Xiao;Yang Li;Qingjun Yu;Shenglan Liu;Jialin Gang

Year: 2022 | Volume: 10 | Journal Article |

Double Attention Network Based on Sparse Sampling

Zhuben Dong;Yunheng Li;Yiwei Sun;Conghui Hao;Kaiyuan Liu;Tao Sun;Shenglan Liu

2022 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Locating action segments in long untrimmed videos is a sub-task of video understanding, which more and more scholars pay attention to. Boundary ambiguity and over-segmentation errors are two difficult problems. To handle them, we propose a network called Double Attention Network based on Sparse Sampling (DASS) on the basis of MS-TCN series. First, we design a Seq2Seq Convolution Sampling Network (...Show More

Double Attention Network Based on Sparse Sampling

Zhuben Dong;Yunheng Li;Yiwei Sun;Conghui Hao;Kaiyuan Liu;Tao Sun;Shenglan Liu

2022 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2022 | Conference Paper |

Spatial Focus Attention for Fine-Grained Skeleton-Based Action Tasks

Kaiyuan Liu;Yunheng Li;Yuanfeng Xu;Shuai Liu;Shenglan Liu

IEEE Signal Processing Letters

Year: 2022 | Volume: 29 | Journal Article |

Cited by: Papers (7)

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Dynamic skeletal data has been widely studied for human action tasks due to its high-level semantic information and less data than RGB features. However, attention-based previous methods fail to focus on the local grouped joint dependence of the human body, which is vital to distinguishing various actions in fine-grained tasks, such as skeletal action segmentation and recognition. This work propos...Show More

Spatial Focus Attention for Fine-Grained Skeleton-Based Action Tasks

Kaiyuan Liu;Yunheng Li;Yuanfeng Xu;Shuai Liu;Shenglan Liu

IEEE Signal Processing Letters

Year: 2022 | Volume: 29 | Journal Article |

Skeleton-Based Action Recognition With Low-Level Features of Adaptive Graph Convolutional Networks

Jialin Gang;Yao Xiao;Shenglan Liu;Yao Lu

Year: 2021 | Volume: 9 | Journal Article |

Cited by: Papers (4)

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Skeleton-based action recognition is a typical classification problem which plays a significant role in human-computer interaction and video understanding. Since a human skeleton has natural graphic features, methods based on graph convolutional networks (GCN) are widely applied in skeleton-based action recognition. Previous studies mainly focus on structural links in GCN to generate high-level fe...Show More

Skeleton-Based Action Recognition With Low-Level Features of Adaptive Graph Convolutional Networks

Jialin Gang;Yao Xiao;Shenglan Liu;Yao Lu

Year: 2021 | Volume: 9 | Journal Article |

Analysis of Intelligent Art Design Paradigm Based on Perception Model and Image Feature Extraction

Xi Fan;Shenglan Liu

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)

Year: 2021 | Conference Paper |

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Analysis of intelligent art design paradigm based on perception model and image feature extraction is studied in this research. In the study of information search behavior, in recent years, most of the measurement of the perceived risk focuses on the user's direct perception to enhance the general model, where the complex paradigm is considered. This research work obtains the locally associated fr...Show More

Analysis of Intelligent Art Design Paradigm Based on Perception Model and Image Feature Extraction

Xi Fan;Shenglan Liu

2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)

Year: 2021 | Conference Paper |

Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval

Shenglan Liu;Muxin Sun;Lin Feng;Hong Qiao;Shuyuan Chen;Yang Liu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2021 | Volume: 32, Issue: 3 | Journal Article |

Cited by: Papers (8)

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A single feature is hard to describe the content of images from an overall perspective, which limits the retrieval performances of single-feature-based methods in image retrieval tasks. To fully describe the properties of images and improve the retrieval performances, multifeature fusion ranking-based methods are proposed. However, the effectiveness of multifeature fusion in image retrieval has no...Show More

Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval

Shenglan Liu;Muxin Sun;Lin Feng;Hong Qiao;Shuyuan Chen;Yang Liu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2021 | Volume: 32, Issue: 3 | Journal Article |

Neural Method for Explicit Mapping of Weighted Locally Linear Embedding in Image Retrieval

Shenglan Liu;Xiang Liu;Yang Wang;Qing Yuan;Huihua Guan;Kaiyuan Liu

2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Year: 2019 | Conference Paper |

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A new explicit nonlinear dimensionality reduction(DR) method, on account of neural networks, is presented for image retrieval tasks. We first propose a Weighted Locally Linear Embedding (WLLE) for training set, based on which linear relations in neighborhood of each sample are guaranteed. Then, a neural method (NM) is proposed to solve the out-of sample problem. As a combination of WLLE and NM, we...Show More

Neural Method for Explicit Mapping of Weighted Locally Linear Embedding in Image Retrieval

Shenglan Liu;Xiang Liu;Yang Wang;Qing Yuan;Huihua Guan;Kaiyuan Liu

2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

Year: 2019 | Conference Paper |

Color Recognition for Rubik's Cube Robot

Lin Feng;Dong Jiang;Aibin Zhang;Shenglan Liu;Feilong Wang;Yang Liu

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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In this paper, we proposed three methods to solve color recognition of Rubik's cube, which includes one offline method and two online methods. Scatter balance & extreme learning machine (SB-ELM), an offline method, is proposed to illustrate the efficiency of training based method. We also put forward a conception of color drifting which indicates offline methods are always ineffectiveness and can ...Show More

Color Recognition for Rubik's Cube Robot

Lin Feng;Dong Jiang;Aibin Zhang;Shenglan Liu;Feilong Wang;Yang Liu

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

Year: 2019 | Conference Paper |

A Fast Online Cascaded Regression Algorithm for Face Alignment

Lin Feng;Caifeng Liu;Shenglan Liu;Huibing Wang

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

Year: 2019 | Conference Paper |

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Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. In many real-time applications, the training data is obt...Show More

A Fast Online Cascaded Regression Algorithm for Face Alignment

Lin Feng;Caifeng Liu;Shenglan Liu;Huibing Wang

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

Year: 2019 | Conference Paper |

Neighborhood Graph Embedding for Nodes Clustering of Social Network

Shuliang Xu;Shenglan Liu;Lin Feng

2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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Graph embedding is an important dimension reduction method for high-dimensional data. In this paper, a neighborhood graph embedding algorithm is proposed and it is applied in data clustering. Different from the traditional graph embedding algorithms, a dependence degree of node is defined and it represents the dependence of two nodes; the adjacency matrix of graph is determined by dependence degre...Show More

Neighborhood Graph Embedding for Nodes Clustering of Social Network

Shuliang Xu;Shenglan Liu;Lin Feng

2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

Year: 2019 | Conference Paper |

Sensor Network Oriented Human Motion Segmentation With Motion Change Measurement

Yang Liu;Lin Feng;Shenglan Liu;Muxin Sun

Year: 2018 | Volume: 6 | Journal Article |

Cited by: Papers (8)

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Smart Internet of Things has greatly improved the quality of human life with increasingly intelligent sensor networks. Efficient and accurate human motion time series segmentation is the key issue in human motion analysis and understanding. To realize human motion sequence segmentation, a comprehensive human motion description and an intelligent segmentation algorithm are required. Hence, this pap...Show More

Sensor Network Oriented Human Motion Segmentation With Motion Change Measurement

Yang Liu;Lin Feng;Shenglan Liu;Muxin Sun

Year: 2018 | Volume: 6 | Journal Article |

Manifold Warp Segmentation of Human Action

Shenglan Liu;Lin Feng;Yang Liu;Hong Qiao;Jun Wu;Wei Wang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2018 | Volume: 29, Issue: 5 | Journal Article |

Cited by: Papers (7)

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Human action segmentation is important for human action analysis, which is a highly active research area. Most segmentation methods are based on clustering or numerical descriptors, which are only related to data, and consider no relationship between the data and physical characteristics of human actions. Physical characteristics of human motions are those that can be directly perceived by human b...Show More

Manifold Warp Segmentation of Human Action

Shenglan Liu;Lin Feng;Yang Liu;Hong Qiao;Jun Wu;Wei Wang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2018 | Volume: 29, Issue: 5 | Journal Article |

Design of vibrating wire sensor signal acquisition board based on STM32

Zhenyu Wu;Shenglan Liu;Meng Du;Qiang Li;Chengda Han;Jiping Wu

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

Cited by: Papers (1)

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In this paper, in order to meet the needs of monitoring engineering such as coal mines, oil fields, dams, bridges, shipbuilding, this paper design vibrating wire sensor signal acquisition board based on the STM32. The structure of the paper is as follow: To begin with this paper introduce the circuit and program design with STM32 as the core. By the analysis of isolation vibration frequency and vi...Show More

Design of vibrating wire sensor signal acquisition board based on STM32

Zhenyu Wu;Shenglan Liu;Meng Du;Qiang Li;Chengda Han;Jiping Wu

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

Robust hashing learning via multi-view subspace learning

Yang Liu;Lin Feng;Shenglan Liu

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

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Hashing learning has attracted increasing attention these years with the explosive increase of data. The hashing learning can be divided into two steps. Firstly, obtain the low dimensional representation of the original data. Secondly, quantize the real number vector of the low dimensional representation of each data point and map them to binary codes. Most of the existing methods measure the orig...Show More

Robust hashing learning via multi-view subspace learning

Yang Liu;Lin Feng;Shenglan Liu

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

Discriminative extreme learning machine to content-based image retrieval with relevance feedback

Xiaodong Huang;Liang Sun;Huihui Guo;Shenglan Liu

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

Cited by: Papers (4)

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To narrow down the semantic gap and increase the retrieval efficiency in image retrieval, relevance feedback (RF) has long been an important approach, where the active support vector machine (SVM) based RFs are widely applied to content-based image retrieval (CBIR). However, the performance of these methods are often poor because the low speed of SVM algorithm in high dimension data. Meanwhile, th...Show More

Discriminative extreme learning machine to content-based image retrieval with relevance feedback

Xiaodong Huang;Liang Sun;Huihui Guo;Shenglan Liu

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

Color binary correlation descriptor for image retrieval

Jun Wu;Shenglan Liu;Lin Feng

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

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In this paper, a novel image descriptor, called Color Binary Correlation (CBC), is proposed for image retrieval. This method defines and describes the structure elements utilizing binary patterns based on colors and edge orientation respectively, and thus integrate texture with the other two properties. Besides, its variants CBCri and CBCu2, which are presented for rotated invariance and “unform” ...Show More

Color binary correlation descriptor for image retrieval

Jun Wu;Shenglan Liu;Lin Feng

2016 12th World Congress on Intelligent Control and Automation (WCICA)

Year: 2016 | Conference Paper |

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