Texture Image Retrieval System Research Articles

Combining Statistical Features and Local Pattern Features for Texture Image Retrieval

The complementary fusion of global and local features can effectively improve the performance of image retrieval. This article proposes a new local texture descriptor, combined with statistical modeling in transform domain for texture image retrieval. The proposed local descriptor calculates the eight directions of the central pixel by using the relationship between the central pixel and the neighboring pixels in six directions, which is called the local eight direction pattern (LEDP). In the texture image retrieval system of this article, the feature extraction part combines global statistical features and local pattern features. Among them, both the relative magnitude (RM) sub-band coefficients and relative phase (RP) sub-band coefficients are modeled as wrapped Cauchy (WC) distribution in the dual-tree complex wavelet transform (DTCWT) domain, and the global statistical features employ the parameters of this model; while the local pattern features respectively choose the local binary pattern (LBP) histogram features in the spatial domain and the LEDP histogram features of each direction sub-band in the DTCWT domain. On the other hand, the similarity measurement selects matching distances for different features and combines them in the form of convex linear optimization. Texture image retrieval experiments are conducted in the Corel-1k database (DB1), Brodatz texture database (DB2) and MIT VisTex texture database (DB3), respectively. Experimental results show that, compared with the best existing methods, the approach proposed in this article has achieved better retrieval performance.

Shift Invariance Level Comparison of Several Contourlet Transforms and Their Texture Image Retrieval Systems

In this paper, we proposed a metric to measure the shift invariance of the three different contourlet transforms. And then, using the same structure texture image retrieval system which use subband coefficients energy, standard deviation and kurtosis features with Canberra distance, we gave a comparison of their texture description abilities. Experimental results show that contourlet-2.3 texture image retrieval system has almost retrieval rates with non-sub sampled contourlet system; the two systems have better retrieval results than the original contourlet retrieval system. On the other hand, for the relatively lower redundancy, we recommend using contourlet- 2.3 as texture description transform.

Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image Retrieval

In this paper, a novel algorithm which integrates the RGB color histogram and texture features for content based image retrieval. A new set of two- dimensional (2-D) M-band dual tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT are designed to improve the texture retrieval performance. Unlike the standard dual tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, two texture databases are used. Further, it is mentioned that the databases used are Brodatz gray scale database and MIT VisTex Color database. The retrieval efficiency and accuracy using proposed features is found to be superior to other existing methods. Index Terms—Color; M-band wavelets; Feature Extraction; M-band dual tree complex wavelets; Image Retrieval.

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

A novel approach for content-based texture image retrieval system using fuzzy logic classifier is proposed in this paper. The novelty of this method is demonstrated by handling the complexity issues in texture image retrieval arising from rotation and scale variance. These issues are divided into four groups as non rotated non scaled, rotation invariant, scale invariant and scale and rotation invariant texture retrieval for retrieval performance analysis. Features of texture images are obtained using discrete wavelet transform based statistical features and gray level co-occurrence matrix based co-occurrence features. The fuzzy logic classifier is developed with Gaussian membership function with mean and standard deviations of the features. The retrieval performance improvement is carried out by considering various combinations of the features. The average retrieval rates for the four issues have been achieved at 99.40% with 40 features, 91% with 80 features, 65.2% with 40 features, and 63.4% with 65 features respectively. This method outperforms the existing methods in terms of average retrieval rate. The scale and rotation invariant texture retrieval is an incomparable work that has been demonstrated in the present paper.

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

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.

Research on Similarity Measurement for Texture Image Retrieval

A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify the retrieval performance of the proposed similarity measurement method. Dual-tree complex wavelet transform and nonsubsampled contourlet transform were used to extract texture features. Experimental results show that the proposed similarity measurement method achieves better retrieval performance than some existing similarity measurement methods.

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

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

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.

Semi-Subsampled Contourlet Retrieval Algorithm Using Three Statistical Features

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-1.3 Texture Retrieval Using Absolute Mean Energy and Kurtosis Features

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.

Wavelet-contourlet texture image retrieval system

Focusing on the problem of low retrieval rate and retrieval speed in contourlet retrieval systems,a wavelet-contourlet transform based texture image retrieval system is proposed.In the system,contourlet transform is carried out for most detailed subbands.Each sub-band energy and standard deviations in transform domain are cascaded to form feature vectors,and the similarity metric is Canberra distance.Comparison is made on contourlet and wavelet-contourlet transform retrieval systems through an experiment on 109 texture images with 640×640 pixels size from Brodatz album.Experimental results show that the latter one has advantages on retrieval rates,retrieval speed and storage room for feature vectors.

Texture Image Retrieval Using New Rotated Complex Wavelet Filters

A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWFs) are designed with complex wavelet filter coefficients, which gives texture information strongly oriented in six different directions (45 degrees apart from complex wavelet transform). The 2-D RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for texture image retrieval by using a set of dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree-complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. The information provided by DT-RCWF complements the information generated by DT-CWT. Features are obtained by computing the energy and standard deviation on each subband of the decomposed image. To check the retrieval performance, texture database D1 of 1856 textures from Brodatz album and database D2 of 640 texture images from VisTex image database is created. Experimental results indicates that the proposed method improves retrieval rate from 69.61% to 77.75% on database D1, and from 64.83% to 82.81% on database D2, in comparing with traditional discrete wavelet transform based approach. The proposed method also retains comparable levels of computational complexity.