Texture segmentation using different orientations of GLCM features

Rampun, Andrik; Strange, Harry; Zwiggelaar, Reyer

doi:10.1145/2466715.2466720

Cited by 28 publications

(23 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8, [19] has the highest predictive accuracy of 90% with 32 features and the highest predictive accuracy achieved is 93.29% for 10 features on Lung Cancer Data [20], whereas in the present study, when 10 features are used, the accuracy achieved is 95.13% using Correlation based feature selection (CFS) for ISDD based model. In the case of ISDD model proposed in this study has achieved the highest accuracy values for 10 features.…”

Section: E Analysis On the Basis Of Number Of Features Selected For mentioning

confidence: 65%

“…On comparing the results of this work with the [19,20] as shown in Fig. 8, [19] has the highest predictive accuracy of 90% with 32 features and the highest predictive accuracy achieved is 93.29% for 10 features on Lung Cancer Data [20], whereas in the present study, when 10 features are used, the accuracy achieved is 95.13% using Correlation based feature selection (CFS) for ISDD based model.…”

Section: E Analysis On the Basis Of Number Of Features Selected For mentioning

confidence: 73%

“…used) as shown in the Tables VII, VIII and IX. On comparing results with textures segmentation model [19], which used Brodatz album (each image size 256 X 256) prepared the gray level co-occurrence matrix at unit pixel distance with angular pixel positions at ϴ =0°, 45, has achieved predictive accuracy as high as 90% (approx.). However this work has prepared the gray level co-occurrence matrix at unit pixel distance with angular pixel positions at ϴ =0°, 45°, 90° and 135° and have achieved better predictive accuracy in all the texture based (ITDD, ITVD, CITDVD) models as shown in Table XIII.…”

Section: A Analysis On the Basis Of Texture Feature Data Setsmentioning

confidence: 99%

See 2 more Smart Citations

An Approach to Improve Classification Accuracy of Leaf Images using Dorsal and Ventral Features

Kumar¹,

Patidar²,

Khazanachi³

et al. 2015

ijacsa

View full text Add to dashboard Cite

Abstract-This paper proposes to improve the classification accuracy of the leaf images by extracting texture and statistical features by utilizing the presence of striking features on the dorsal and ventral sides of the leaves, which on other types of objects may not be that prominent. The texture features have been extracted from dorsal, ventral and a combination of dorsalventral sides of leaf images using Gray level co-occurrence matrix. In addition to this, this work also uses certain general statistical features for discriminating them into various classes. The feature selection work has been performed separately for the dorsal, ventral and combined data sets (for both texture and statistical features) using the most common feature selection algorithms.After selecting the relevant features, the classification has been done using the classification algorithms: K-Nearest Neighbor, J48, Naïve Bayes, Partial Least Square (PLS), Classification and Regression Tree (CART), Classification Tree(CT). The classification accuracy has been calculated and compared to find which side of the leaf image (dorsal or ventral) gives better results with which type of features(texture or statistical). This study reveals that the ventral leaf features can be another alternative in discriminating the leaf images into various classes.

show abstract

Section: E Analysis On the Basis Of Number Of Features Selected For mentioning

confidence: 65%

Section: E Analysis On the Basis Of Number Of Features Selected For mentioning

confidence: 73%

Section: A Analysis On the Basis Of Texture Feature Data Setsmentioning

confidence: 99%

See 1 more Smart Citation

An Approach to Improve Classification Accuracy of Leaf Images using Dorsal and Ventral Features

Kumar¹,

Patidar²,

Khazanachi³

et al. 2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…Features are extracted based on certain characteristics of the co-occurrence matrix and then fingerprint classification is done using neural networks. Rampun et al (2013) proposed new texture-based segmentation algorithm which uses a set of features extracted from grey-level co-occurrence matrices. Principal component analysis is used to reduce the dimensionality of the resulting feature space.…”

Section: Introductionmentioning

confidence: 99%

Intramodal palmprint recognition using texture feature

Latha¹,

Prasad²

2017

IJISDC

View full text Add to dashboard Cite

Palmprint technology is a new branch of biometrics used to identify an individual. Palmprint has rich set of features like palm lines, wrinkles, minutiae points, texture, ridges, etc. Several line and texture extraction techniques for palmprint have been extensively studied. This paper presents an intramodal authentication system based on texture information extracted from the palmprint using the Haralick features, 2D-Gabor and 2D-log Gabor filters. An individual feature vector is computed for a palmprint using the extracted texture information of each filter type. Performance of the system using three feature types is evaluated individually. Finally, we combine the three feature types using feature level fusion to develop an intramodal palmprint recognition system. The experiments are evaluated on a standard benchmark database (PolyU Database), and the results shows that significant improvement in terms of recognition accuracy and error rates with the proposed intramodal recognition system compared to individual representations.

show abstract

“…Spectral and spatial features such as colour, texture, edges and shape are widely used image-based features for segmentation (Tian, 2013). Among them texture features from GLCM are often reported as key features to infer the radiometric characteristics of the surface (Rampun et al, 2013). Numerous segmentation approaches are used in practice such as regionbased approach (e.g., region growing, split and merge), clustering-based approach (e.g., k-means, mean shift), and graph-based approach (e.g., graph-cut) (Boykov and Funka-Lea, 2006;Narkhede, 2013).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Segmentation of Uav-Based Images Incorporating 3d Point Cloud Information

Vetrivel

Gerke

Kerle

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Numerous applications related to urban scene analysis demand automatic recognition of buildings and distinct sub-elements. For example, if LiDAR data is available, only 3D information could be leveraged for the segmentation. However, this poses several risks, for instance, the in-plane objects cannot be distinguished from their surroundings. On the other hand, if only image based segmentation is performed, the geometric features (e.g., normal orientation, planarity) are not readily available. This renders the task of detecting the distinct sub-elements of the building with similar radiometric characteristic infeasible. In this paper the individual sub-elements of buildings are recognized through sub-segmentation of the building using geometric and radiometric characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV) images are used for inferring the geometric characteristics of roofs and facades of the building. However, the image-based 3D points are noisy, error prone and often contain gaps. Hence the segmentation in 3D space is not appropriate. Therefore, we propose to perform segmentation in image space using geometric features from the 3D point cloud along with the radiometric features. The initial detection of buildings in 3D point cloud is followed by the segmentation in image space using the region growing approach by utilizing various radiometric and 3D point cloud features. The developed method was tested using two data sets obtained with UAV images with a ground resolution of around 1-2 cm. The developed method accurately segmented most of the building elements when compared to the plane-based segmentation using 3D point cloud alone.

show abstract

Texture segmentation using different orientations of GLCM features

Cited by 28 publications

References 27 publications

An Approach to Improve Classification Accuracy of Leaf Images using Dorsal and Ventral Features

An Approach to Improve Classification Accuracy of Leaf Images using Dorsal and Ventral Features

Intramodal palmprint recognition using texture feature

Segmentation of Uav-Based Images Incorporating 3d Point Cloud Information

Contact Info

Product

Resources

About