Evaluation of texture features for automatic detecting butterfly species using extreme learning machine

Kaya, Yılmaz; Kaycı, Lokman; Tekin, Ramazan; Ertuğrul, Ömer Faruk

doi:10.1080/0952813x.2013.861875

Cited by 46 publications

(15 citation statements)

References 16 publications

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“…Additionally, 92.85% accuracy was obtained by GLCM with ANN methods for 14 butterfly species methods [27]. Furthermore, we employed GLCM and local binary pattern (LBP), the highest accuracy of identification of 19 butterfly species was obtained for these feature extraction methods is observed while employing ELM with 98.25%, 96.45% accuracy, respectively, and the accuracy obtained for ANN are 93.16% and 89.47%, respectively [29]. Therefore, the obtained accuracy results of two descriptors were acceptable depending on the accuracy obtained in comparison to LBP and the results in previous studies.…”

Section: Comparison Of Nlbp D Albp˛and Traditional Lbp Operatorsmentioning

confidence: 99%

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Two novel local binary pattern descriptors for texture analysis

Kaya

Ertuğrul

Tekin

2015

Applied Soft Computing

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Section: Comparison Of Nlbp D Albp˛and Traditional Lbp Operatorsmentioning

confidence: 99%

“…The butterflies were collected in Van (Turkey) between May, 2002 and August 2003, and in the attitudes of 1800-3200 m [27][28][29], are illustrated in Fig. 3.…”

Section: Datasetsmentioning

confidence: 99%

Two novel local binary pattern descriptors for texture analysis

Kaya

Ertuğrul

Tekin

2015

Applied Soft Computing

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“…For classification accuracy of butterfly images, ELM classifier has also been deployed. In the modern era, Convolutional neural networks (CNN) have also been utilized for the various functions related to image analysis [28].…”

Section: B Related State-of-the-art Workmentioning

confidence: 99%

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

Shikha¹,

Gitanjali²,

Kumar³

2020

IJIMAI

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The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.

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“…To comparatively evaluate the proposed approach, FM and WM were performed on real world data sets of different specifications [23][24][25][26][27][28][29][30], and were analyzed in terms of both accuracy and computational cost. The motivation behind the use of WM was the success of the best accuracy [17].…”

Section: Employed Proceduresmentioning

confidence: 99%

A fast feature selection approach based on extreme learning machine and coefficient of variation

Ertuğrul¹,

Tağluk²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Feature selection is the method of reducing the size of data without degrading their accuracy. In this study, we propose a novel feature selection approach, based on extreme learning machines (ELMs) and the coefficient of variation (CV). In the proposed approach, the most relevant features are identified by ranking each feature with the coefficient obtained through ELM divided by CV. The achieved accuracies and computational costs, obtained with the use of features selected via the proposed approach in 9 classification and 26 regression benchmark data sets, were compared to those obtained with all features, as well as those obtained with the features selected by a wrapper and a filtering method. The achieved accuracy values obtained with the proposed approach were generally higher than when using all features. Furthermore, high feature reduction ratios were obtained with the proposed approach, including the achieved feature reduction ratios in epilepsy, liver, EMG, shuttle, and abalone. Stock data sets were 90.48%, 90%, 70.59%, 66.67%, 75%, and 77.78%, respectively. This approach is an extremely fast process that is independent of the employed machine-learning methods.

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Evaluation of texture features for automatic detecting butterfly species using extreme learning machine

Cited by 46 publications

References 16 publications

Two novel local binary pattern descriptors for texture analysis

Two novel local binary pattern descriptors for texture analysis

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

A fast feature selection approach based on extreme learning machine and coefficient of variation

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