Multicodebook Neural Network Using Intelligent K-Means Clustering Based on Histogram Information for Multimodal Data Classification

Marsum, M. Anwar; Arsa, Dewa Made Sri; Hermawan, Indra; Jatmiko, Wisnu; Nurhadiyatna, Adi

doi:10.1109/iwbis.2018.8471699

Cited by 3 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These characteristics allowed for an effective prediction capacity and also resulted in less overfitting (Berk, 2013). Moreover, for remote‐sensing approaches, parametric classifiers such as DA, GLM and nB have shown limitations in dealing with multimodal distributions (Liu et al ., 2011), whereas, by contrast, nonparametric classifiers such as k NN, NN, SVM, DT and RF have shown better results under multimodal distributions (Maulik and Chakraborty, 2017; Marsum et al ., 2018).…”

Section: Discussionmentioning

confidence: 99%

Automatic wheat ear counting using machine learning based on RGB UAV imagery

et al. 2020

View full text Add to dashboard Cite

In wheat and other cereals, the number of ears per unit area is one of the main yield determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time-consuming. An automatic ear counting system is proposed using machine learning techniques based on RGB images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with 3 nitrogen treatments during the 2017-2018 crop season. The automatic system uses a frequency filter, segmentation, and feature extraction with different classification techniques to discriminate wheat ears in micro-plot images. The relationship between the image-based manual counting and the algorithm counting exhibited high accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in-situ ear counting with grain yield were also compared. Correlations between both ear counting systems were strong, particularly for the lower N treatment.Methodological requirements and limitations are discussed.

show abstract

Section: Discussionmentioning

confidence: 99%

Automatic wheat ear counting using machine learning based on RGB UAV imagery

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A popular intelligent clustering method is intelligent K-means clustering based on anomalous patterns. Another version is intelligent clustering based on histogram information, which utilized in a previous study [21].…”

Section: Intelligent Clusteringmentioning

confidence: 99%

“…In that study, we used K-means [18], a Gaussian mixture model (GMM) [19], and intelligent K-means based on anomalous pattern clustering [20]. In another study, we tried to search for alternative intelligent clustering for generating multi-codebook neural networks [21]. In the previous study, we proposed a multi-codebook based on an intelligent K-means method based on histogram information.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we propose two types of multi-codebook fuzzy neural networks for multi-modal data classification, one using intelligent clustering and the other using dynamic incremental learning. The first contribution of this study is the further investigation into the multi-codebook neural networks based on intelligent K-means clustering that have been previously proposed [14,21]. The second contribution is the proposal of new versions of multi-codebook neural networks that use dynamic incremental learning, which is hereafter referred to as the multi-codebook fuzzy neural networks that use version 2 of incremental learning.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Clustering and Dynamic Incremental Learning to Generate Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Ma’sum

2020

Symmetry

View full text Add to dashboard Cite

Classification in multi-modal data is one of the challenges in the machine learning field. The multi-modal data need special treatment as its features are distributed in several areas. This study proposes multi-codebook fuzzy neural networks by using intelligent clustering and dynamic incremental learning for multi-modal data classification. In this study, we utilized intelligent K-means clustering based on anomalous patterns and intelligent K-means clustering based on histogram information. In this study, clustering is used to generate codebook candidates before the training process, while incremental learning is utilized when the condition to generate a new codebook is sufficient. The condition to generate a new codebook in incremental learning is based on the similarity of the winner class and other classes. The proposed method was evaluated in synthetic and benchmark datasets. The experiment results showed that the proposed multi-codebook fuzzy neural networks that use dynamic incremental learning have significant improvements compared to the original fuzzy neural networks. The improvements were 15.65%, 5.31% and 11.42% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively, for incremental version 1. The incremental learning version 2 improved by 21.08% 4.63%, and 14.35% on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively. The multi-codebook fuzzy neural networks that use intelligent clustering also had significant improvements compared to the original fuzzy neural networks, achieving 23.90%, 2.10%, and 15.02% improvements on the synthetic dataset, the benchmark dataset, and the average of all datasets, respectively.

show abstract

“…In the study, we utilized K-Means clustering, intelligent K-Means clustering based on anomalous patterns, and a parametric version of Gaussian mixture model (GMM) clustering [18]- [20]. In the previous study, we also tried to develop a variant of intelligent K-Means clustering to enhance the neural network's performance [21]. In the previous study, we applied two variants of intelligent K-Means clustering to generate the multi-codebook neural network models [15].…”

Section: Introductionmentioning

confidence: 99%

Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification

2024

Self Cite

View full text Add to dashboard Cite

One of the open challenges in machine learning is multi-modal data classification. A classifier model needs to be enhanced to deal with multi-modal data. This study is proposed to develop multi-codebook neural networks using intelligent Gaussian mixture model clustering for multi-modal data classification. The intelligent Gaussian mixture model clustering is developed in this study prior to the development of multicodebook models. The method analyzes the gradient of input data histogram to find the number of generated mixtures and cluster the data. The proposed multi-codebook neural network model has three variants based on the rules to fund the number of clusters. The experiment result showed that the proposed three variants of multi-codebook models performed well in the synthetic and benchmark datasets. The proposed model improved the original method by 24.14%, 15.97%, and 3.71% accuracy and 0.3510, 0.0487, and 0.2031 kappa for synthetic datasets, benchmark datasets, and overall datasets respectively. By using the ANOVA test, we have proved that all three variants of the proposed multi-codebook neural network were proved to have significant improvements compared to the original version.INDEX TERMS multi-modal, classification, multi-codebook, neural networks, intelligent clustering, Gaussian mixture model, histogram.

show abstract

Multicodebook Neural Network Using Intelligent K-Means Clustering Based on Histogram Information for Multimodal Data Classification

Cited by 3 publications

References 11 publications

Automatic wheat ear counting using machine learning based on RGB UAV imagery

Automatic wheat ear counting using machine learning based on RGB UAV imagery

Intelligent Clustering and Dynamic Incremental Learning to Generate Multi-Codebook Fuzzy Neural Network for Multi-Modal Data Classification

Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification

Contact Info

Product

Resources

About