Texture Based Image Retrieval Using Framelet Transform–Gray Level Co-occurrence Matrix(GLCM)

Sulochana, S.; Vidhya, R.

doi:10.14569/ijarai.2013.020211

Cited by 14 publications

(10 citation statements)

References 27 publications

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“…It is a statistical approach for texture extraction in many fields and used alone or synergistically with other analysis to evaluate the images morphology. Therefore, According to the advantage of co-occurrence matrix framework [17], GLCM is then taken as the descriptor to statist the property of LBPs description in this paper.…”

Section: Texture-related Algorithmsmentioning

confidence: 99%

Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images

Wei

Zhang

2016

Sensors

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Real-time and accurate detection of the sailing or water area will help realize unmanned surface vehicle (USV) systems. Although there are some methods for using optical images in USV-oriented environmental modeling, both the robustness and precision of these published waterline detection methods are comparatively low for a real USV system moving in a complicated environment. This paper proposes an efficient waterline detection method based on structure extraction and texture analysis with respect to optical images and presents a practical application to a USV system for validation. First, the basic principles of local binary patterns (LBPs) and gray level co-occurrence matrix (GLCM) were analyzed, and their advantages were integrated to calculate the texture information of river images. Then, structure extraction was introduced to preprocess the original river images so that the textures resulting from USV motion, wind, and illumination are removed. In the practical application, the waterlines of many images captured by the USV system moving along an inland river were detected with the proposed method, and the results were compared with those of edge detection and super pixel segmentation. The experimental results showed that the proposed algorithm is effective and robust. The average error of the proposed method was 1.84 pixels, and the mean square deviation was 4.57 pixels.

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Section: Texture-related Algorithmsmentioning

confidence: 99%

Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images

Wei

Zhang

2016

Sensors

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“…This study uses Gray Level Co-occurrence Matrix for extract features texture and Extreme learning machine as classification. Gray Level Co-occurrence Matrix (GLCM) was first introduced by Haralick to extract texture featuresand perform image analysis based on the statistical distribution of pixel intensity [1]. Extreme Learning Machine (ELM) is a new learning method of the neural network.…”

Section: Introductionmentioning

confidence: 99%

Signature Identification with Gray Level Co-ccurrence Matrix and Extreme Learning Machine

Sinaga¹,

Tulus²,

Budhiarti³

et al. 2019

Proceedings of the Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January

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Identification of the signature is a process used to identify the signature of a person. The Identification of the signature can be divided into two parts. Identification signature off-line and the Identification of signatures on-line. Forgery of signatures is still common in data security systems. So, we need an approach to improve the accuracy of signature recognition on Extreme Learning Machine algorithms. This study use a gray level co-occurrence matrix (GLCM) for feature extraction and modification of Extreme Learning Machine (ELM) for recognition. ELM is a new learning method of a neural network or commonly called the Single Hidden Layer Feed forward Neural Networks (SLFNs). From the experiments the identification of the signature using the gray level co-occurrence matrix (GLCM) and signature classification using extreme learning machine with the addition of elementary transformations showed that the accuracy on identification of the signature is 43% using of features contrast, correlation, energy and homogeneity, while using just ELM accuracy is 36%.

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“…This approach explored gray level spatial reliant of texture.Tamura et al [5] explored texture depiction from unlike angle and projected a computational estimation on six visual properties like coarsness,disparity,directionality,linelikeness,constancy and unevenness. The QBIC system and MARS system further improved Tamura's texture depiction [1].…”

Section: Introductionmentioning

confidence: 99%

A new approach towards Image retrieval using texture statistical methods

Tamilkodi

Kumari

Maruthuperumal³

2018

JOIV : Int. J. Inform. Visualization

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Abstract-Texture is a possession that represents the facade and arrangement of an image. Image textures are intricate ocular patterns serene of entities or regions with sub-patterns with the kind of brightness, color, outline, dimension, and etc.This article proposes a new method for texture characterization by using statistical methods (TCUSM). In this proposed method (TCUSM) the features are obtained from energy, entropy, contrast and homogeneity. In an image, each one pixel is enclosed by 8 nearest pixels. The confined in turn for a pixel can be extracted from a neighbourhood of 3x3 pixels, which represents the fewest absolute unit. We used four vector angles 0, 45, 90,135 to carry out the experimentation with the query image. A total of 16 texture values are calculated per unit. Compute the feature vectors for the query image by calculating texture unit and the resultant value is compared with the image database. The retrieval result shows that the performance using Canberra distance has achieved higher performance.

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Texture Based Image Retrieval Using Framelet Transform–Gray Level Co-occurrence Matrix(GLCM)

Cited by 14 publications

References 27 publications

Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images

Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images

Signature Identification with Gray Level Co-ccurrence Matrix and Extreme Learning Machine

A new approach towards Image retrieval using texture statistical methods

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