A local binary pattern based texture descriptors for classification of tea leaves

Tang, Zhe; Su, Yuancheng; Er, Meng Joo; Qi, Fang; Zhang, Li; Zhou, Jing

doi:10.1016/j.neucom.2015.05.024

Cited by 106 publications

(49 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Naresh and Nagendraswamy [14] modified the conventional Local Binary Patterns (LBP) approach to consider the structural relationship between neighboring pixels, replacing the hard threshold approach of basic LBP. Tang et al [15] introduced a new texture extraction method, based on the combination of Gray Level Co-Occurrence Matrix (GLCM) and LBP, to classify tea leaves. Venation.…”

Section: Related Studiesmentioning

confidence: 99%

“…For this reason, researchers in computer vision have used leaves as a comparative tool to classify plants [7,8,9,10]. Characters such as shape [11,12,13], texture [14,15,16] and venation [17,18] are the features most generally used to distinguish the leaves of different species. The history of plant identification methods, however shows that existing plant identification solutions are highly dependent on the ability of experts to encode domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

462

172

View full text Add to dashboard Cite

Highlights• A deep learning approach to quantify discriminatory leaf is proposed.• Shape is not a dominant feature for leaf but rather the different orders of venation.• Deep learning reveals transformation of leaf features from general to specific types.• Findings archived fit with the hierarchical botanical definitions of leaf characters• Features learned using deep learning can improve plant recognition performance. AbstractPlant identification systems developed by computer vision researchers have helped botanists to recognize and identify unknown plant species more rapidly. Hitherto, numerous studies have focused on procedures or algorithms that maximize the use of leaf databases for plant predictive modeling, but this results in leaf features which are liable to change with different leaf data and feature extraction techniques. In this paper, we learn useful leaf features directly from the raw representations of input data using Convolutional Neural Networks (CNN), and gain intuition of the chosen features based on a Deconvolutional Network (DN) approach. We report somewhat unexpected results: (1) different orders of venation are the best representative features compared to those of outline shape, and (2) we observe multi-level representation in leaf data, demonstrating the hierarchical transformation of features from lower-level to higher-level abstraction, corresponding to species classes. We show that these findings fit with the hierarchical botanical definitions of leaf characters. Through these findings, we gained insights into the design of new hybrid feature extraction models which are able to further improve the discriminative power of plant classification systems. The source code and models are available at: https://github.com/cs-chan/Deep-Plant.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How deep learning extracts and learns leaf features for plant classification

Lee

Chan

Mayo

et al. 2017

Pattern Recognition

462

172

View full text Add to dashboard Cite

show abstract

“…Local features extracting methods usually filter an image with a given operator, such as Gabor [7][8][9], Fourier transforms [10], Hough transforms [11], self-feedback template [12], LBP (Local Binary Pattern) [13][14], Wavelet [15][16] and other filters [17][18], etc. In [7], the authors used Log-Gabor filter to detect the edge-oriented urban characteristics, and two Log-Gabor filter response images to suppress the noise and acquire a smooth spatial region.…”

Section: Research Statusesmentioning

confidence: 99%

Building Objects Extracting and Recognizing Methodology from Satellite Image based on the Feature Extraction Algorithms and Sliding Window Technique

Yang¹,

Shen²

2016

IJSIP

View full text Add to dashboard Cite

show abstract

“…Zhang et al [25] combined three different types of features (shape, color, and texture) to identify fruit images. Tang et al [26] extracted features from green tea leaves by combining local binary pattern (LBP) and gray level co-occurrence matrix (GLCM). Akar and Gungor [27] integrated multiple texture methods and normalized difference vegetation index (NDVI) to the random forest algorithm, in order to detect tea and hazelnut plantation areas.…”

Section: Introductionmentioning

confidence: 99%

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Zhang

Yang

Cattani

et al. 2016

Entropy

View full text Add to dashboard Cite

This work proposes a tea-category identification (TCI) system, which can automatically determine tea category from images captured by a 3 charge-coupled device (CCD) digital camera. Three-hundred tea images were acquired as the dataset. Apart from the 64 traditional color histogram features that were extracted, we also introduced a relatively new feature as fractional Fourier entropy (FRFE) and extracted 25 FRFE features from each tea image. Furthermore, the kernel principal component analysis (KPCA) was harnessed to reduce 64 + 25 = 89 features. The four reduced features were fed into a feedforward neural network (FNN). Its optimal weights were obtained by Jaya algorithm. The 10ˆ10-fold stratified cross-validation (SCV) showed that our TCI system obtains an overall average sensitivity rate of 97.9%, which was higher than seven existing approaches. In addition, we used only four features less than or equal to state-of-the-art approaches. Our proposed system is efficient in terms of tea-category identification.

show abstract

A local binary pattern based texture descriptors for classification of tea leaves

Cited by 106 publications

References 28 publications

How deep learning extracts and learns leaf features for plant classification

How deep learning extracts and learns leaf features for plant classification

Building Objects Extracting and Recognizing Methodology from Satellite Image based on the Feature Extraction Algorithms and Sliding Window Technique

Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm

Contact Info

Product

Resources

About