scholarly journals Multi-feature based Foggy Image Classification

2019 ◽  
Vol 234 ◽  
pp. 012089
Author(s):  
Yan Chen ◽  
Jiangtao Wang ◽  
Shuai Li ◽  
Weiwei Wang
Keyword(s):  
Image Classification ◽  
Feature Based

scholarly journals Extracting image features for classification by two-tier genetic programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
A Song ◽  
K Neshatian ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Domain Knowledge ◽  
Extraction Process ◽  
High Accuracy ◽  
Classification Performance ◽  
Image Features ◽  
Classification Methods ◽  
Feature Based ◽  
Second Tier

Image classification is a complex but important task especially in the areas of machine vision and image analysis such as remote sensing and face recognition. One of the challenges in image classification is finding an optimal set of features for a particular task because the choice of features has direct impact on the classification performance. However the goodness of a feature is highly problem dependent and often domain knowledge is required. To address these issues we introduce a Genetic Programming (GP) based image classification method, Two-Tier GP, which directly operates on raw pixels rather than features. The first tier in a classifier is for automatically defining features based on raw image input, while the second tier makes decision. Compared to conventional feature based image classification methods, Two-Tier GP achieved better accuracies on a range of different tasks. Furthermore by using the features defined by the first tier of these Two-Tier GP classifiers, conventional classification methods obtained higher accuracies than classifying on manually designed features. Analysis on evolved Two-Tier image classifiers shows that there are genuine features captured in the programs and the mechanism of achieving high accuracy can be revealed. The Two-Tier GP method has clear advantages in image classification, such as high accuracy, good interpretability and the removal of explicit feature extraction process. © 2012 IEEE.

Hyperspectral Image Classification via Multiple-Feature-Based Improved Sparse Representation

2019 ◽  
Vol 39 (5) ◽  
pp. 0528004
Author(s):  
李非燕 Li Feiyan ◽  
霍宏涛 Huo Hongtao ◽  
李静 Li Jing ◽  
白杰 Bai Jie
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Multiple Feature ◽  
Feature Based

Deep and wide feature based extreme learning machine for image classification

2020 ◽  
Vol 412 ◽  
pp. 426-436 ◽  
Author(s):  
Yuanyuan Qing ◽  
Yijie Zeng ◽  
Yue Li ◽  
Guang-Bin Huang
Keyword(s):  
Image Classification ◽  
Extreme Learning Machine ◽  
Feature Based ◽  
Learning Machine

scholarly journals High Dimensional Feature for Hyperspectral Image Classification

2018 ◽  
Vol 246 ◽  
pp. 03041
Author(s):  
Cailing Wang ◽  
Hongwei Wang ◽  
Yinyong Zhang ◽  
Jia Wen ◽  
Fan Yang
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Texture Feature ◽  
High Dimensional ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Feature Based ◽  
Pca Method ◽  
The One ◽  
Low Dimensional

Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage. In this paper, we study the performance of a high-dimensional feature by texture feature. The texture feature based on multi-local binary pattern descriptor, can achieve significant improvements over both its tradition version and the one we proposed in our previous work. We also make the high-dimensional feature practical, we employ the PCA method for dimension reduction and support vector machine for hyperspectral image classification. The two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the high dimensional feature can enhance the classification accuracy than some low dimensional.

scholarly journals Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

2019 ◽  
Vol 11 (23) ◽  
pp. 2870
Author(s):  
Chu He ◽  
Qingyi Zhang ◽  
Tao Qu ◽  
Dingwen Wang ◽  
Mingsheng Liao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Feature Fusion ◽  
Texture Features ◽  
Texture Image ◽  
Binary Coding ◽  
Deep Feature ◽  
Feature Based ◽  
Texture Image Classification

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms.

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