Material Texture Contourlet Retrieval by Energy and Variance Distribution Features

2012 ◽  
Vol 562-564 ◽  
pp. 208-211
Author(s):  
Xin Wu Chen ◽  
Jing Ge ◽  
Li Ping Che
Keyword(s):  
Retrieval System ◽  
Contourlet Transform ◽  
Image Texture ◽  
Hardware Complexity ◽  
System A ◽  
Retrieval Rate ◽  
Distribution Features ◽  
Material Texture ◽  
New Feature ◽  
Canberra Distance

contourlet transform can extract image texture information more efficiently than wavelet 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 contourlet transform retrieval system, a new feature named variance distribution was proposed and a 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.

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.

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.

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.

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.

Contourlet-1.3 Texture Retrieval Using Absolute Mean Energy and Kurtosis Features

2011 ◽  
Vol 48-49 ◽  
pp. 327-330
Author(s):  
Xin Wu Chen ◽  
Li Wei Liu
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Texture Image ◽  
Feature Vectors ◽  
System A ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Canberra Distance ◽  
Texture Retrieval

To improve the texture image retrieval rate of contourlet texture image retrieval system, a contourlet-1.3 transform based texture image retrieval system was proposed. In the system, the contourlet transform was contourlet-1.3, a new version of the original contourlet, sub-bands absolute mean energy and kurtosis in each contourlet-1.3 sub-band were cascaded to form feature vectors, and the similarity metric was Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean energy and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean energy which is most commonly used today under same dimension of feature vectors. Contourlet-1.3 transform based image retrieval system is superior to those of the original contourlet, non-subsampled contourlet and contourlet-2.3 systems under same system structure with same dimension of feature vectors, retrieval time and memory needed.

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.

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.

Contourlet-S Retrieval Algorithm Using Absolute Mean Energy and Kurtosis Features

2012 ◽  
Vol 197 ◽  
pp. 473-476 ◽  
Author(s):  
Xiang Ying Li ◽  
Rui Xue ◽  
Xin Wu Chen ◽  
Wei Luo
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Contourlet Transform ◽  
Directional Information ◽  
Feature Vectors ◽  
Retrieval Rate ◽  
S Transform ◽  
Decomposition Structure ◽  
Image Retrieval System

Contourlet transform has better performance in directional information representation than wavelet transform and has been studied by many researchers in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, a contourlet-S transform based texture image retrieval system was proposed in this paper. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands absolute mean energy and kurtosis in contourlet-S 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 features cascaded by absolute mean and kurtosis 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. contourlet-S 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, contourlet-S decomposition structure parameters can make significant effects on retrieval rates, especially scale number.

Contourlet-S Texture Image Retrieval System

2012 ◽  
Vol 433-440 ◽  
pp. 3408-3412
Author(s):  
Jian Zhong Ma ◽  
Xin Wu Chen ◽  
Li Juan Zhong
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Contourlet Transform ◽  
Texture Image ◽  
Feature Vectors ◽  
Retrieval Rate ◽  
S Transform ◽  
Decomposition Structure ◽  
Image Retrieval System

Contourlet transform is better in direction information representation than wavelet transform which has been studied in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, an anti-aliasing contourlet-S transform based texture image retrieval system was proposed. In this system, the contourlet transform was constructed by anti-aliasing non-subsampled Laplacian pyramid cascaded by critical sub-sampled directional filter banks, sub-bands energy and standard deviations in contourlet domain are cascaded to form feature vectors, and the similarity metric used here is Canberra distance. Experimental results show that contourlet-S transform based image retrieval system is superior to those of the original contourlet transform, and non-subsampled contourlet system under the same system structure with almost same dimension of feature vectors, retrieval time and memory needed; and contourlet decomposition structure parameters can make significant effects on retrieval rates, especially scale number. To improve the retrieval rate of this system, kurtosis in each sub-band coefficients can be incorporated in features at the cost of some higher dimension of feature vectors.

New Feature Extraction Method Based on Contourlet Transform for Banknote Classification

2013 ◽  
Vol 321-324 ◽  
pp. 1061-1065
Author(s):  
Guo Wei Yang ◽  
Wen Ling Wang ◽  
Shan Gai
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Extraction Methods ◽  
Image Feature ◽  
Contourlet Transform ◽  
Image Texture ◽  
Statistical Characteristics ◽  
Image Feature Extraction ◽  
Feature Extraction Method ◽  
New Feature

In order to improve the performance of the banknote classification, new banknote image feature extraction method is proposed in this paper. The contourlet transform is applied to the original banknote image which is obtained by image contact sensor.The statistical characteristics of transformed image in the contourlet domain are analyzed. The statistical characteristics which can perfectly reflect the banknote image texture information are used as feature vector for banknote classification. The experimental results show that the proposed method can obtain higher recognition compared with other conventional banknote image feature extraction methods.

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