Dimensionality Reduction Based on Feature Selection for Rice Varieties Recognition

Duong, Huu-Thanh; Hoang, Vinh Truong

doi:10.1109/incit.2019.8912121

Cited by 14 publications

(15 citation statements)

References 20 publications

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“…Duong and Hoang [62] presented a method based on Histogram of Oriented Gradient (HOG) descriptor and feature selection method to classify the rice quality. They extracted HOG features from the rice image, and the score of each feature was calculated by the fisher score feature selection method.…”

Section: A Feature Selectuion Methodsmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: A Feature Selectuion Methodsmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

View full text Add to dashboard Cite

show abstract

“…They also mentioned that this feature is very much sensitive to the room environment as well as lighting conditions. To overcome this problem, scientists proposed different textural features [1,3,12,28,46,47,51] including Haralick [3,6], HOG [19,41], GIST [28], and SIFT [28]. Most of the textural feature generates a long vector which leads to increase system time complexity.…”

Section: Introductionmentioning

confidence: 99%

“…From the literature review of the mentioned studies, it is easy to say that the ANN outperformed other methods. However, the performance of classification techniques can be increased by reducing the time using different feature selection methods [16,19,20,61].…”

Section: Introductionmentioning

confidence: 99%

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Paddy seed variety identification using T20-HOG and Haralick textural features

Uddin

Islam

Shajalal

et al. 2021

Complex Intell. Syst.

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The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.

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“…Among of them, histogram of oriented gradients (HOG) [15] is successfully applied for image classification and object detection. Duong and Hoang [16] apply to extract rice seed images based on features coded in multiple color spaces using HOG descriptor. Phan et al [17] evaluate and compare different local image descriptors (GIST, SIFT, morphological features) and classifier (random forest, KNN, SVM) for rice seed varieties identification.…”

Section: Introductionmentioning

confidence: 99%

Rice seed image classiﬁcation based on HOG descriptor with missing values imputation

Quoc

Hoang

2020

TELKOMNIKA

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Rice is a primary source of food consumed by almost half of world population. Rice quality mainly depends on the purity of the rice seed. In order to ensure the purity of rice variety, the recognition process is an essential stage. In this paper, we firstly propose to use histogram of oriented gradient (HOG) descriptor to characterize rice seed images. Since the size of image is totally random and the features extracted by HOG can not be used directly by classifier due to the different dimensions. We apply several imputation methods to fill the missing data for HOG descriptor. The experiment is applied on the VNRICE benchmark dataset to evaluate the proposed approach.

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Dimensionality Reduction Based on Feature Selection for Rice Varieties Recognition

Cited by 14 publications

References 20 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Paddy seed variety identification using T20-HOG and Haralick textural features

Rice seed image classiﬁcation based on HOG descriptor with missing values imputation

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