The texture classification using the fusion of decisions from different texture classifiers

Pharsook, S.; Kasetkasem, Teerasit; Larmsrichan, P.; Siddhichai, Supakorn; Chanwimaluang, Thitiporn; Isshiki, Takaaki

doi:10.1109/ecticon.2011.5948012

Cited by 3 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ARF calculated the sum of weight pixel intensities of neighboring pixels (Materka and Strzelecki, 1998). COMF examined the relationship between pixels over a selected image area (Pharsook et al, 2011). GF in an image was the force at every point, giving the direction of the biggest possible increment from light to dim and the rate of change in that direction (Raju et al, 2014 andDrzewiecki et al, 2013).…”

Section: Fig-2: Sample Preprocessed Leaf Imagementioning

confidence: 99%

Automated Classification of Hair Care Plants Using Geometrical and Textural Features From Leaf Images: A Pattern Recognition Based Approach

Shaukat

2023

PJS

View full text Add to dashboard Cite

Automated classification plays a vital role in content based image retrieval systemsin addition to many more. Inter-class similarity and intra-class dissimilarity is the main challengeposed by leaf classification. This research work proposed a plant classification system using texturaland geometrical features from leaf images. Six classification models, among which three wereensemble methods, were considered to evaluate the accuracy of proposed technique. Train and teststrategy was adopted to evaluate the performance of different classifiers. Experimental results showedthat the proposed technique outperformed the state of the art. Moreover, it was observed that texturalfeatures outperformed geometrical features. The best accuracy achieved with textural features was100%, whereas it was 98.8% when geometrical features were used. SVM, IBk and Random Treeremained the best classifiers in leaf identification using both types of features. Hence, textural andgeometrical features could be effectively used for plant classification

show abstract

Section: Fig-2: Sample Preprocessed Leaf Imagementioning

confidence: 99%

Automated Classification of Hair Care Plants Using Geometrical and Textural Features From Leaf Images: A Pattern Recognition Based Approach

Shaukat

2023

PJS

View full text Add to dashboard Cite

show abstract

“…For example, [11] examined the Random Forest (RF) classifier to improve the image segmentation method and measure their importance in classification. [12] used the support vector machine technique to classify five texture images based on the prospect weighted incorporation method of three feature extraction methods, including the Gray-Level Co-occurrence Matrix, Semivariogram Function and Gaussian Markov Random Fields. [13] proposed approaches that evaluated on the brodatz and CUReT databases and compared various texture classification methods.…”

Section: Extraction Of Texturementioning

confidence: 99%

Atexture Classification Using Random Forest And Decision Tree

Razooq¹

2015

VAWKUM trans. comput. sci.

View full text Add to dashboard Cite

Textural and Geometrical Features Based Approach for Identification of Individuals Using Palmprint and Hand Shape Images from Multiple Multimodal Datasets

Shaukat

Farhan

Fahiem

et al. 2018

Journal of Testing and Evaluation

View full text Add to dashboard Cite

Identification and classification of biometrics are important research areas in the field of image processing and pattern recognition. Biometrics are the measurement and statistical analysis of physiological and behavioral characteristics of humans. A wide variety of biometric modalities are available, with unimodal biometrics suffering from several factors. The proposed research is novel because it uses a single image of a hand in order to extract a variety of unique characteristics, like hand shape and the palmprint associated with individual hands. Moreover, it obtains higher accuracy with minimum effort. We have chosen the multimodal biometrics, i.e., palmprint and hand shape, from three datasets, i.e., PolyU Palmprint Database, GPDS Hand Database, and the Bosphorus Hand Database, for a total of 1,072 images. There are 302 textural features found in the palmprint images, and 12 geometrical features are extracted from the hand images. Classification models include Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (IBk), Decision Tree, Random Tree, Random Forest, and Bagging. The train and test method is used to evaluate the performance of different classifiers. It is observed that Naïve Bayes, SVM, IBk, and Random Tree models result in classification accuracy of 99.44 % with palmprint images using the 302 textural features over the combined dataset. After feature reduction, similar accuracy is achieved with the top ten, and even with the top five, features. For geometrical features, an accuracy of 99.81 % is achieved with the hand images using Naïve Bayes, SVM, IBk, and Random Tree.

show abstract

The texture classification using the fusion of decisions from different texture classifiers

Cited by 3 publications

References 5 publications

Automated Classification of Hair Care Plants Using Geometrical and Textural Features From Leaf Images: A Pattern Recognition Based Approach

Automated Classification of Hair Care Plants Using Geometrical and Textural Features From Leaf Images: A Pattern Recognition Based Approach

Atexture Classification Using Random Forest And Decision Tree

Textural and Geometrical Features Based Approach for Identification of Individuals Using Palmprint and Hand Shape Images from Multiple Multimodal Datasets

Contact Info

Product

Resources

About