scholarly journals Efficient Image Set Classification Using Linear Regression Based Image Reconstruction

2017 ◽  
Author(s):  
Syed A. A. Shah ◽  
Uzair Nadeem ◽  
Mohammed Bennamoun ◽  
Ferdous Sohel ◽  
Roberto Togneri
Keyword(s):  
Image Reconstruction ◽  
Linear Regression ◽  
Image Set Classification ◽  
Image Set

scholarly journals Simultaneous Image Reconstruction and Feature Learning with 3D-CNNs for Image Set–Based Classification

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Xinyu Zhang ◽  
Xiaocui Li ◽  
Xiao-Yuan Jing ◽  
Li Cheng
Keyword(s):  
Image Reconstruction ◽  
State Of The Art ◽  
Feature Learning ◽  
Temporal Information ◽  
Classification Methods ◽  
Learning Network ◽  
Image Set Classification ◽  
Image Set ◽  
Deep Image ◽  
3D Cnn

Image set–based classification has attracted substantial research interest because of its broad applications. Recently, lots of methods based on feature learning or dictionary learning have been developed to solve this problem, and some of them have made gratifying achievements. However, most of them transform the image set into a 2D matrix or use 2D convolutional neural networks (CNNs) for feature learning, so the spatial and temporal information is missing. At the same time, these methods extract features from original images in which there may exist huge intra-class diversity. To explore a possible solution to these issues, we propose a simultaneous image reconstruction with deep learning and feature learning with 3D-CNNs (SIRFL) for image set classification. The proposed SIRFL approach consists of a deep image reconstruction network and a 3D-CNN-based feature learning network. The deep image reconstruction network is used to reduce the diversity of images from the same set, and the feature learning network can effectively retain spatial and temporal information by using 3D-CNNs. Extensive experimental results on five widely used datasets show that our SIRFL approach is a strong competitor for the state-of-the-art image set classification methods.

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

Low-rank regularized tensor discriminant representation for image set classification

2019 ◽  
Vol 156 ◽  
pp. 62-70 ◽  
Author(s):  
Peiguang Jing ◽  
Yuting Su ◽  
Zhengnan Li ◽  
Jing Liu ◽  
Liqiang Nie
Keyword(s):  
Low Rank ◽  
Image Set Classification ◽  
Image Set

Regularized constraint subspace based method for image set classification

2018 ◽  
Vol 76 ◽  
pp. 434-448 ◽  
Author(s):  
Hengliang Tan ◽  
Ying Gao ◽  
Zhengming Ma
Keyword(s):  
Image Set Classification ◽  
Image Set
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