Texture Classification Based on Empirical Wavelet Transform Using LBP Features

2017 ◽  
Vol 8 (3) ◽  
pp. 623
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Wavelet Transform ◽  
Classification Accuracy ◽  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Automatic Inspection ◽  
Gray Scale ◽  
Novel Approach ◽  
Empirical Wavelet Transform ◽  
Inspection Systems

<p>Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.</p>

scholarly journals Unsupervised Local Binary Pattern Histogram Selection Scores for Color Texture Classification

2018 ◽  
Vol 4 (10) ◽  
pp. 112 ◽  
Author(s):  
Mariam Kalakech ◽  
Alice Porebski ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Color Image ◽  
Texture Classification ◽  
Classification Problems ◽  
Class Label ◽  
Laplacian Score ◽  
Pattern Histogram ◽  
Color Texture

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

A novel approach for automated detection of focal EEG signals using empirical wavelet transform

2016 ◽  
Vol 29 (8) ◽  
pp. 47-57 ◽  
Author(s):  
Abhijit Bhattacharyya ◽  
Manish Sharma ◽  
Ram Bilas Pachori ◽  
Pradip Sircar ◽  
U. Rajendra Acharya
Keyword(s):  
Wavelet Transform ◽  
Automated Detection ◽  
Eeg Signals ◽  
Novel Approach ◽  
Empirical Wavelet Transform

A Novel Approach in Adopting Finite State Automata for Image Processing Applications

2018 ◽  
Vol 8 (1) ◽  
pp. 59-74
Author(s):  
R. Obulakonda Reddy ◽  
Kashyap D. Dhruve ◽  
R. Nagarjuna Reddy ◽  
M. Radha ◽  
N. Sree Vani
Keyword(s):  
Image Processing ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Texture Classification ◽  
Superior Performance ◽  
Finite State Automata ◽  
Processing Application ◽  
Novel Approach ◽  
Comparison Results ◽  
Finite State

This article describes how robust image processing application rely heavily on image descriptors extracted. Limited work is carried out in adopting probabilistic finite state automata (PFSA) models for image processing. A finite state automata for image processing (FSAFIP) method is presented here. Texture classification and content based image retrieval (CBIR) is considered. In FSAFIP, foreground and background regions of an image are identified and later split into patches. Using a tristate PFSA model, feature descriptors corresponding to background/foreground regions are constructed. A distance based large margin nearest neighbor (LMNN) classifier is considered in FSAFIP to impart intelligence. A performance and experimental study to evaluate performance of FSAFIP for CBIR and texture classification is presented. Comparison results in CBIR obtained prove superior performance of FSAFIP over existing methods on Corel-1K dataset. High texture classification accuracy of 99.2% is reported using FSAFIP on KHT-TIPS dataset. An improved texture classification accuracy is achieved using FSAFIP in comparison to former methods.

scholarly journals Completed Local Ternary Pattern for Rotation Invariant Texture Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Taha H. Rassem ◽  
Bee Ee Khoo
Keyword(s):  
Classification Accuracy ◽  
Local Binary Pattern ◽  
Texture Classification ◽  
Experimental Results ◽  
Rotation Invariant ◽  
Local Ternary Pattern ◽  
Binary Count ◽  
Robust To Noise

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors.

DWT and Lifting Wavelet Transform Based Robust Watermarking for Color Image

2015 ◽  
Vol 10 (11) ◽  
pp. 1127
Author(s):  
Nidaa Hasan Abbas ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Abed Rahman Bin Ramli ◽  
Sajida Parveen
Keyword(s):  
Wavelet Transform ◽  
Color Image ◽  
Robust Watermarking ◽  
Lifting Wavelet Transform ◽  
Lifting Wavelet
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