Manesh Kokare

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/spl deg/ 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.

Comparison of similarity metrics for texture image retrieval

Similarity metrics plays an important role in content-based image retrieval. The paper compares nine image similarity measures - Manhattan (L1), weighted-mean-variance (WMV), Euclidean (L2), Chebychev (L/spl infin/), Mahalanobis, Canberra, Bray-Curtis, squared chord and squared chi-squared distances - for texture image retrieval. A large texture database of 1856 images, derived from the Brodatz album, is used to check the retrieval performance. Features of all the database images were extracted using the Gabor wavelet. Experimental results on the Brodatz texture database indicate that the retrieval performance can be improved significantly by using the Canberra and Bray-Curtis distance metrics as compare to traditional Euclidean and Mahalanobis distance based approaches.

Texture image retrieval using rotated wavelet filters

A novel approach for texture image retrieval is proposed by using a new set of two-dimensional (2-D) rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly. A new set of 2-D rotated wavelet improves characterization of diagonally oriented textures. Experimental results indicate that the proposed method improves retrieval rate from 70.09% to 78.44% on database D1, and from 75.62% to 80.78% on database D2, compared with the traditional DWT based approach. The proposed method also retains comparable levels of computational complexity.

A Survey on Current Content based Image Retrieval Methods

Retrieving information from the Web is becoming a common practice for internet users. However, the size and heterogeneity of the Web challenge the effectiveness of classical information retrieval techniques. Content-based retrieval of images and video has become a hot research area. The reason for this is the fact that we need effective and efficient techniques that meet user requirements, to access large volumes of digital images and video data. This paper gives a brief survey of current CBIR (Content Based Image Retrieval) methods and technical achievement in this area. The survey includes a large number of papers covering the research aspects of system design and applications of CBIR, image feature representation and extraction, Multidimensional indexing. Furthermore future research directions are suggested.

Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters

This paper proposes a novel approach for rotation-invariant texture image retrieval by using set of dual-tree rotated complex wavelet filter (DT-RCWF) and DT complex wavelet transform (DT-CWT) jointly, which obtains texture features in 12 different directions. Two-dimensional RCWFs are nonseparable and oriented, which improves characterization of oriented textures. Robust and efficient isotropic rotationally invariant features are extracted from DT-RCWF and DT-CWT decomposed subbands. This paper demonstrates the effectiveness of this new set of features on four different sets of rotated and nonrotated databases. Experimental results indicate that the proposed method improves retrieval accuracy from 83.17% to 93.71% on a small size (208 images) nonrotated database D1, from 82.71% to 90.86% on a small size (208 images) rotated database D2, from 72.18% to 76.09% on a medium-size (640 images) rotated database D3, and from 64.17% to 78.93% on a large size (1856 images) rotated database D4, compared with the discrete wavelet transform-based approach. New method also retains complexity

Cosine-modulated wavelet based texture features for content-based image retrieval

Feature extraction is one of the most important tasks for efficient and accurate image retrieval purpose. In this paper we have presented a Cosine-modulated wavelet transform based technique for extraction of texture features. The major advantages of Cosine-modulated wavelet transform are less implementation complexity, good filter quality, and ease in imposing the regularity conditions. Texture features are obtained by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, texture database of 1856 textures is created from Brodatz album. Retrieval efficiency and accuracy using Cosine-modulated wavelet based features is found to be superior to other existing methods.

Cosine-Modulated Wavelet Packet based Texture Features for Content based Image Retrieval

Content-based image retrieval (CBIR) is an active research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular texture features for CBIR. We propose Cosine modulated wavelet packet based texture features for CBIR. The major advantages of Cosine modulated wavelet transform are less implementation complexity and good filter quality. Texture features are obtained by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, texture database of 1856 textures is created from Brodatz album. Results are compared with standard Daubechies wavelet packet, Af-band wavelet packet and Gabor wavelet. Result indicates that proposed method is superior to other methods in terms of retrieval accuracy and retrieval time.

Rotated complex wavelet based texture features for content based image retrieval

In this paper we have proposed a novel approach of extracting texture features for content-based image retrieval. A new set of two-dimensional (2-D) rotated complex wavelet filters (RCWF) is designed with complex wavelet filter coefficients. 2-D RCWF are nonseparable and oriented, which improves characterization of oriented textures. Dual-tree rotated complex wavelet filter (DT-RCWF) and dual-tree complex wavelet transform (DT-CWT) are used jointly for texture analysis in twelve different directions. Texture features are obtained by computing the energy and standard deviation of each subband. Retrieval results obtained using each individual method and in combination are presented. Retrieval performance obtained with the combined filterbank is superior relative to the performance obtained using the other existing methods. New method also retains comparable levels of computational complexity.

Wavelets for content based image retrieval and digital watermarking for multimedia applications

In this paper we have proposed novel wavelet-based texture image retrieval and digital image watermarking techniques. For texture image retrieval we have used standard deviation and energy as a feature measure and Canberra distance metric as dissimilarity measure. Large texture database of 1856 images is used to check the retrieval performance. Proposed retrieval method is far superior to the traditional method, which uses wavelet coefficients energy as a feature and Euclidean distance metric as dissimilarity measure. We have proposed two watermarking algorithms. In first proposed method a binary seal is embedded into the region of interest. This method is robust against lossy compression, distortion due to noise and cropping. In second algorithm spread spectrum technique is used to embed the watermark. This method is robust against median filtering and scaling.

Dimensionality reduction of tree structured wavelet transform texture features for content based image retrieval

Dimensionality reduction methods are of interest in applications such as content-based image and video retrieval. The focus of this paper is on the dimensionality reduction of feature vectors of tree structured wavelet decomposition for improving the retrieval speed. We have investigated a novel idea of reduction of feature dimension by concatenating the inter scale approximate coefficients and intra scale detail coefficient of each individual channel. The results are quite impressive; in an experiment using Brodatz texture database, feature vector length is reduced by a factor of three, which doubles retrieval speed without significantly reducing retrieval performance.