A survey on applications and variants of the cuckoo search algorithm

http://lms.ctl.cyut.edu.tw/sysdata/8/21108/doc/d7b84166985e286b/attach/905844.pdf

A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm

This paper presents a robust image watermarking scheme for multimedia copyright protection. In this work, host image is partitioned into four sub images. Watermark image such as ‘logo’ is embedded in the two of these sub images, in both D (singular and diagonal matrix) and U (left singular and orthogonal matrix) components of Singular Value Decomposition (SVD) of two sub images. Watermark image is embedded in the D component using Dither quantization. A copy of the watermark is embedded in the columns of U matrix using comparison of the coefficients of U matrix with respect to the watermark image. If extraction of watermark from D matrix is not complete, there is a fair amount of probability that it can be extracted from U matrix. The proposed algorithm is more secure and robust to various attacks, viz., JPEG2000 compression, JPEG compression, rotation, scaling, cropping, row-column blanking, row-column copying, salt and pepper noise, filtering and gamma correction. Superior experimental results are observed with the proposed algorithm over a recent scheme proposed by Chung et al. in terms of Bit Error Rate (BER), Normalized Cross correlation (NC) and Peak Signal to Noise Ratio (PSNR).

A Robust Image Watermarking Scheme using Singular Value Decomposition

Various content-based image retrieval techniques are available for retrieving the require and classify images, we are reviewing them. In our first section, we are tending towards some basics of a particular CBIR system with that we have shown some basic features of any image, these are like shape, texture, color and shown different techniques to calculate them. In the next section, we have shown different distance measuring techniques used for similarity measurement of any image and also discussed indexing techniques. Finally conclusion and future scope is discussed.

A Survey on Content Based Image Retrieval Systems

In the past decade, several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion. This paper proposes a novel multiplicative watermarking scheme in the contourlet domain. The effectiveness of a watermark detector depends highly on the modeling of the transform-domain coefficients. In view of this, we first investigate the modeling of the contourlet coefficients by the alpha-stable distributions. It is shown that the univariate alpha-stable distribution fits the empirical data more accurately than the formerly used distributions, such as the generalized Gaussian and Laplacian, do. We also show that the bivariate alpha-stable distribution can capture the across scale dependencies of the contourlet coefficients. Motivated by the modeling results, a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha-stable distributions. It is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized Gaussian distribution does. However, a watermark detector designed based on the alpha-stable distribution with a value of its parameter α other than 1 or 2 is computationally expensive because of the lack of a closed-form expression for the distribution in this case. Therefore, a watermark detector is designed based on the bivariate Cauchy member of the alpha-stable family for which α = 1 . The resulting design yields a significantly reduced-complexity detector and provides a performance that is much superior to that of the GG detector and very close to that of the detector corresponding to the best-fit alpha-stable distribution. The robustness of the proposed bivariate Cauchy detector against various kinds of attacks, such as noise, filtering, and compression, is studied and shown to be superior to that of the generalized Gaussian detector.

A study of multiplicative watermark detection in the contourlet domain using alpha-stable distributions.

Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).

/pdf/content-based-image-retrieval-using-exact-legendre-moments-154xq29yfa.pdf

Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

/pdf/content-based-image-retrieval-using-exact-legendre-moments-4tlqji8uyu.pdf

Summary A novel oblivious and highly robust watermarking scheme using Multiple Descriptions (MD) and Quantization Index Modulation (QIM) of the host image is presented in this paper. The watermark is embedded in the Discrete Contourlet Transform domain (CT). Discrete Countourlet Transform (CT) is able to capture the directional edges and contours superior to Discrete Wavelet Transform (DWT). Watermark embedding is done at two stages for achieving robustness to various attacks. This algorithm is highly robust for different attacks on the watermarked image and superior in terms of Peak Signal to Noise Ratio (PSNR) and Normalized Cross correlation (NC). here the part of summary.

http://paper.ijcsns.org/07_book/200802/20080206.pdf

B. Chandra Mohan

Papers

A Robust Image Watermarking Scheme using Singular Value Decomposition

Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

Robust Digital Watermarking Scheme using Contourlet Transform

The performance of a hybrid routing intelligent algorithm in a mobile ad hoc network