Ekstraksi Fitur Tekstur dan Warna pada Kulit Katak Menggunakan GLCM dan Momen Warna

Anura is an order in the Amphibian class consisting of frogs and toads. Anura is very important in the ecosystem, especially its role as part of the food chain. Anura's main role is to maintain the balance of the ecosystem and as a bioindicator agent for changing environmental conditions such as water pollution, habitat destruction, disease and parasites, and climate change. This research applies digital image processing technology which is expected to assist in detecting types of frogs based on color and texture. This research uses 5 types of frogs, namely kongkang gading, kongkang poison, striped trees, small trees and flying trees with 20 images of each type of frog. This research uses the color feature extraction methods such Color Moment and texture extraction GLCM (Gray Level Co-occurance Matrix), then classified using K-Star. The results of the K-Star performance evaluation to classify the 5 types of frogs obtained the Accuracy (Acc) value of 0.93, Precision (Prec) of 0.94, Recall (Rec) of 0.93 and F-measure of 0.93. So that the classification results of frog species on texture and color feature extraction using the GLCM method and the Color Moment with the K-Star classification method have high performance and can work well.

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GPU-accelerated image segmentation based on level sets and multiple texture features

Multimedia Tools and Applications ◽

10.1007/s11042-020-09911-5 ◽

2020 ◽

Author(s):

Daniel Reska ◽

Marek Kretowski

Keyword(s):

Feature Extraction ◽

Image Segmentation ◽

Level Set ◽

High Performance ◽

Texture Features ◽

Extraction Methods ◽

Multi Stage ◽

Multiple Feature ◽

Feature Extraction And Selection ◽

Contour Evolution

Abstract In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

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COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW

Iraqi Journal for Computers and Informatics ◽

10.25195/ijci.v45i1.45 ◽

2019 ◽

Vol 45 (1) ◽

pp. 15-19

Author(s):

Sarmad Abdul-samad

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Information Extraction ◽

Spatial Information ◽

Extraction Methods ◽

Content Based Image Retrieval ◽

Visual Features ◽

Visual Content ◽

Color Feature ◽

Color Correlogram

Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors featurestaking in consideration the spatial information of the image.

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New improved feature extraction methods for real-time high performance image sequence recognition

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing ◽

10.1109/icassp.1997.595396 ◽

2002 ◽

Cited By ~ 7

Author(s):

G. Rigoll ◽

A. Kosmala

Keyword(s):

Feature Extraction ◽

Real Time ◽

High Performance ◽

Image Sequence ◽

Extraction Methods ◽

Sequence Recognition

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Comparison of color-based feature extraction methods in banana leaf diseases classification using SVM and K-NN

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v24.i3.pp1523-1533 ◽

2021 ◽

Vol 24 (3) ◽

pp. 1523

Author(s):

Nur Sholehah Mat Said ◽

Hizmawati Madzin ◽

Siti Khadijah Ali ◽

Ng Seng Beng

Keyword(s):

Feature Extraction ◽

Research Paper ◽

Extraction Methods ◽

Plant Diseases ◽

Experimental Result ◽

Support Vector ◽

Svm Classifier ◽

Color Feature ◽

Hsv Histogram ◽

Banana Leaf

In Malaysia, banana is a top fruit production which contribute to the economy growth in agriculture field. Hence, it is significant to have a quality production of banana and important to detect the plant diseases at the early stage. There are many types of banana leaf diseases such as Banana Mosaic, Black Sigatoka and Yellow Sigatoka. These three diseases are related to color changes at banana. This research paper is an experiment based and need to identify the best color feature extraction method to classify banana leaf diseases. Total of 48 banana leaf images that are used in this research paper. Four types of color feature extraction methods which are color histogram, color moment, hue, saturation, and value (HSV) histogram and color auto correlogram are experimented to determine the best method for banana leaf diseases classification. While for the classifiers, support vector machine (SVM) and k-Nearest neighbors (k-NN) are used to evaluate the performance and accuracy of each color feature extraction methods. There are also preliminary experiments to identify accurate parameters to use during classification for both classifiers. Our experimental result express that HSV histogram is the best method to classify banana leaf diseases with 83.33% of accuracy and SVM classifier perform better compared to k-NN.

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A Novel Feature Extraction Method Based on Collaborative Representation for Face Recognition

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415560042 ◽

2015 ◽

Vol 29 (04) ◽

pp. 1556004 ◽

Cited By ~ 3

Author(s):

Wei Huang ◽

Xiaohui Wang ◽

Jianzhong Li ◽

Zhong Jin

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Extraction Method ◽

High Performance ◽

Feature Space ◽

Extraction Methods ◽

Collaborative Representation ◽

Feature Extraction Method ◽

Discriminative Feature ◽

New Feature

Representation-based classification have received much attention in the field of face recognition. Collaborative representation-based classification (CRC) has shown the robustness and high performance. In this paper, we proposed a new feature extraction method-based collaborative representation. Firstly, we get the coefficients of all face samples by collaborative representation. Then we define the inter-class reconstructive errors and intra-class reconstructive errors for each sample. After that, Fisher criterion is used to get the discriminative feature. At last, CRC is executed to get the identification results in the new feature space. Different from other feature extraction methods, the proposed method integrates the classification criterion into the feature extraction. So the feature space we get fits the classifier better. Experiment results on several face databases show that the proposed method is more effective than other state-of-the-art face recognition methods.

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