Non-Subsampled Contourlet Texture Retrieval Using Four Estimators

2012 ◽  
Vol 263-266 ◽  
pp. 167-170 ◽  
Author(s):  
Xin Wu Chen ◽  
Jing Ge ◽  
Jin Gen Liu
Keyword(s):  
Wavelet Transform ◽  
Standard Deviation ◽  
Digital Images ◽  
Contourlet Transform ◽  
Retrieval Algorithm ◽  
Retrieval Rate ◽  
Retrieval Systems ◽  
Texture Information ◽  
Shift Invariance ◽  
Texture Retrieval

Contourlet transform is superior to wavelet transform in representing texture information and sparser in describing geometric structures in digital images, but lack of robust character of shift invariance. Non-subsampled contourlet transform (NSCT) alleviates this shortcoming hence more suitable for texture and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rates of existing contourlet transforms retrieval systems, a new texture retrieval algorithm was proposed. In the algorithm, texture information was represented by four statistical estimators, namely, L2-energy, kurtosis, standard deviation and L1-energy of each sub-band coefficients in NSCT domain. Experimental results show that the new algorithm can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today.

Material Texture Retrieval Using Contourlet-2.3 and Three Statistical Features

2011 ◽  
Vol 233-235 ◽  
pp. 2495-2498 ◽  
Author(s):  
Xin Wu Chen ◽  
Zhan Qing Ma
Keyword(s):  
Standard Deviation ◽  
Retrieval System ◽  
Contourlet Transform ◽  
Retrieval Algorithm ◽  
System A ◽  
Retrieval Rate ◽  
Single Feature ◽  
Material Texture ◽  
Canberra Distance ◽  
Texture Retrieval

To improve the retrieval rate of contourlet transform texture retrieval system, a contourlet-2.3 transform based retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurtosis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a contourlet-2.3 retrieval system was proposed. The feature vectors were constructed by cascading the standard deviation, absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that the new retrieval algorithm can lead to a higher retrieval rate than several contourlet transform retrieval systems including the original contourlet transform, non-subsampled contourlet transform under the same structure.

Semi-Subsampled Contourlet Retrieval Algorithm Using Three Statistical Features

2012 ◽  
Vol 433-440 ◽  
pp. 3117-3123
Author(s):  
Yu Xi Liu ◽  
Xin Wu Chen
Keyword(s):  
Standard Deviation ◽  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Contourlet Transform ◽  
Retrieval Algorithm ◽  
Feature Vectors ◽  
Retrieval Rate ◽  
Decomposition Structure ◽  
Image Retrieval System

In order to improve the retrieval rate of contourlet transform retrieval system, a semi-subsampled contourlet transform based texture image retrieval system was proposed. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands standard deviation, absolute mean energy and kurtosis in semi-subsampled contourlet domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the three cascaded features can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. Semi-subsampled contourlet transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, decomposition structure parameters can also make significant effects on retrieval rates, especially scale number.

Wavelet-Contourlet Retrieval Using Energy and Kurtosis Features

2011 ◽  
Vol 201-203 ◽  
pp. 2330-2333
Author(s):  
Xin Wu Chen ◽  
Zhan Qing Ma ◽  
Li Wei Liu
Keyword(s):  
Standard Deviation ◽  
Retrieval System ◽  
Feature Vector ◽  
Contourlet Transform ◽  
Feature Vectors ◽  
System A ◽  
Retrieval Rate ◽  
Retrieval Systems ◽  
Single Feature ◽  
Canberra Distance

To improve the retrieval rate of contourlet transform retrieval system and reduce the redundancy of contourlet which cost two much time in building feature vector database, a new wavelet-contourlet transform retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurotis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a new contourlet retrieval system was proposed. The feature vectors were constructed by cascading the absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than several contourlet transform retrieval systems which utilize the combination feature of standard deviation and absolute mean energy most commonly used today under same dimension of feature vectors.

Comparison of Two Texture Retrieval Algorithms

2012 ◽  
Vol 182-183 ◽  
pp. 1962-1966 ◽  
Author(s):  
Xin Wu Chen ◽  
Yi Xian Shen ◽  
Shi Xin Ma
Keyword(s):  
Image Processing ◽  
Standard Deviation ◽  
Image Retrieval ◽  
Texture Feature ◽  
Contourlet Transform ◽  
Texture Image ◽  
Retrieval Systems ◽  
Retrieval Algorithms ◽  
Canberra Distance ◽  
Texture Retrieval

Contourlet transform and wavelet have been widely used in image processing systems including texture image retrieval applications, and many literatures have reported how to construct the retrieval systems. Among them, generalize Gaussian distribution (GGD) model is a promising one. We will compare the algorithm with another one, which uses energy and standard deviation features and Canberra distance. Experimental results on 40 texture images from MIT vision database show that the latter one has higher retrieval rates for wavelet and contourlet transform retrieval systems, which indicate that the GGD model is not accurate for charactering texture feature.

Variance Distribution and Energy Featured Non-Subsampled Contourlet Texture Retrieval

2012 ◽  
Vol 472-475 ◽  
pp. 893-896
Author(s):  
Xin Wu Chen ◽  
Hua Cheng Xie ◽  
Jin Gen Liu
Keyword(s):  
Retrieval System ◽  
Contourlet Transform ◽  
Image Texture ◽  
Hardware Complexity ◽  
System A ◽  
Retrieval Rate ◽  
Texture Information ◽  
New Feature ◽  
Canberra Distance ◽  
Texture Retrieval

Non-Subsampled contourlet transform can extract image texture information more efficiently than basic contourlet transform and has been studied for image de-noising, enhancement, and retrieval situations, its low retrieval rate are still not satisfied due to feature extraction and other reasons. Focus on improving the retrieval rate of non-subsampled contourlet transform retrieval system, a new feature named variance distribution was proposed and then a non-subsampled contourlet retrieval system was constructed in this paper. The feature vectors were constructed by cascading the energy and variance distribution of each sub-band coefficients and the similarity measure used here was Canberra distance. Experimental results show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and hardware complexity.

Contourlet-1.3 Texture Retrieval with Energy, Standard Deviation and Kurtosis

2013 ◽  
Vol 446-447 ◽  
pp. 1347-1352
Author(s):  
Zhen Guo ◽  
Xin Wu Chen
Keyword(s):  
Standard Deviation ◽  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Contourlet Transform ◽  
Directional Information ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Canberra Distance ◽  
Texture Retrieval

Contourlet-1.3 transform has better performance in directional information representation than the original contourlet transform due to less artifacts and local frequency characteristics, and has been studied by us in retrieval systems and has been shown it is superior to contourlet ones at retrieval rate. In order to improve the retrieval rate further, a novel contourlet-1.3 transform based texture image retrieval system was proposed in this paper. In the system, sub-bands energy, standard deviation and kurtosis in contourlet domain were cascaded to form feature vectors, and the similarity measure function was Canberra distance. Experimental results show that this contourlet-1.3 transform based image retrieval system has higher retrieval rates about 7% to that of the contourlet transform with absolute mean sub-bands energy and standard deviations features under same system structure.

Contourlet-1.3 Texture Retrieval Algorithm by Sub-Bands Energy and Consistency

2012 ◽  
Vol 220-223 ◽  
pp. 2684-2687
Author(s):  
Jing Ge ◽  
Xin Wu Chen
Keyword(s):  
Retrieval System ◽  
Feature Vector ◽  
System Structure ◽  
Image Texture ◽  
Retrieval Algorithm ◽  
Measure Function ◽  
System A ◽  
Retrieval Rate ◽  
Canberra Distance ◽  
Texture Retrieval

Contourlet-1.3 transform has fewer artifacts than original contourlet transform proposed by Do in 2002; it can extract image texture information more efficiently and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rate of contourlet-1.3 transform retrieval system, a new contourlet-1.3 texture retrieval algorithm was proposed in this paper. The feature vector of this system was a combination of sub-band energy and consistency and the similarity measure function used here was Canberra distance. Experimental results on 109 texture images coming from Brodatz album show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and system structure.

FINITE IMPULSE RESPONSE DOUBLE DENSITY FILTER BANKS AND FRAMELETS

2013 ◽  
Vol 11 (01) ◽  
pp. 1350010
Author(s):  
ASHOKA JAYAWARDENA ◽  
PAUL KWAN
Keyword(s):  
Wavelet Transform ◽  
Impulse Response ◽  
Filter Bank ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Wavelet Filters ◽  
Density Filter ◽  
Shift Invariance ◽  
Factorization Methods ◽  
Invariant Properties

In this paper, we focus on the design of oversampled filter banks and the resulting framelets. The framelets obtained exhibit improved shift invariant properties over decimated wavelet transform. Shift invariance has applications in many areas, particularly denoising, coding and compression. Our contribution here is on filter bank completion. In addition, we propose novel factorization methods to design wavelet filters from given scaling filters.

scholarly journals Analisis Total Electron Content (TEC) Menggunakan Continous Wavelet Transform Sebagai Indikator Prekursor Gempa Bumi Di Wilayah Provinsi Bengkulu

2019 ◽  
Vol 9 (2) ◽  
pp. 27-33
Author(s):  
Laksamana Agung Aprillo ◽  
Hendy Santosa ◽  
Faisal Hadi
Keyword(s):  
Wavelet Transform ◽  
Standard Deviation ◽  
Total Electron Content ◽  
Electromagnetic Waves ◽  
Electron Content ◽  
Observation Data ◽  
Dual Frequency ◽  
Total Electron ◽  
Gps Observation ◽  
Large Earthquakes

ABSTRACT Bengkulu is one of 34 provinces in Indonesia which is a megathrust region. So Bengkulu province is often hit by many large earthquakes with shallow depth. TEC anomaly was analyzed based on three electromagnetic waves radiated by an earthquake. The total electron content (TEC) anomaly is seen through the global positioning system (GPS) dual-frequency radio signal data. The continuous wavelet transform (CWT) method is used to divide the signal analysis into several sections according to the electromagnetic wave frequency range of acoustic (2.5 mHz) -3 mHz), gravity waves (1 mHz-2.8 mHz) and rayleigh waves (5 mHz-33 mHz). GPS observation data for 9 days is calculated using the Standard deviation (2?) method to see trends in data changes. The analysis shows anomalies in the September 12 2007 earthquake (7.9 Mw), the March 5 2010 earthquake (6.3 Mw) and the August 4 2011 earthquake (6.0 Mw). Anomalies are detected 1 to 5 hours before an earthquake occurs. TEC anomalies that occur may be related to the process of preseismic before the earthquake and may be an early sign of an earthquake.Keyword: earthquake, total electron content, continous wavelet transform, standard deviation

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