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Jing-wei Gao

Bio: Jing-wei Gao is an academic researcher from Hebei Normal University. The author has contributed to research in topics: Digital image processing & Rough set. The author has an hindex of 2, co-authored 2 publications receiving 11 citations.

Papers
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Proceedings ArticleDOI
22 Jan 2009
TL;DR: An algorithm that gray image as watermark is embedded in original color-image is introduced that thinks over both unobtrusiveness and robustness, and the size of image is unlimited.
Abstract: Recently, as the fast development of the computer network communication technology, it’s more and more convenient for the digital information storage and transmission. Therefore, more and more attention is focused on the copyright protection of digital information. An algorithm that gray image as watermark is embedded in original color-image is introduced. This algorithm thinks over both unobtrusiveness and robustness, and the size of image is unlimited. So it has strongly practical significance.

7 citations

Proceedings ArticleDOI
22 Jan 2009
TL;DR: The rough set is brought into fuzzy cluster by using the methods of attributes contracted in the rough set theory to improve the FCM algorithm; the improved algorithm had been proved a high precise ratio.
Abstract: Clustering is used to find out the objects that resemble each other and compose different groups, cluster analysis is an important job in data mining. Thisarticle brings the rough set into fuzzy cluster, by using the methods of attributes contracted in the rough set theory to improve the FCM algorithm; the improvedalgorithm had been proved a high precise ratio.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: A Novel Weighted Fuzzy C-Means clustering method based on Immune Genetic Algorithm (IGA-NWFCM) is proposed and hence it improves the performance of the existing techniques to solve the high dimensional multi-class problems.

39 citations

Journal ArticleDOI
TL;DR: In this article, an interval number is introduced for attribute weighting in the weighted fuzzy c-means (WFCM) clustering, and it is illustrated that interval weighting can obtain appropriate weights more easily from the viewpoint of geometric probability.
Abstract: The fuzzy c-means (FCM) algorithm is a widely applied clustering technique, but the implicit assumption that each attribute of the object data has equal importance affects the clustering performance. At present, attribute weighted fuzzy clustering has became a very active area of research, and numerous approaches that develop numerical weights have been combined into fuzzy clustering. In this paper, interval number is introduced for attribute weighting in the weighted fuzzy c-means (WFCM) clustering, and it is illustrated that interval weighting can obtain appropriate weights more easily from the viewpoint of geometric probability. Moreover, a genetic heuristic strategy for attribute weight searching is proposed to guide the alternating optimization (AO) of WFCM, and improved attribute weights in interval-constrained ranges and reasonable data partition can be obtained simultaneously. The experimental results demonstrate that the proposed algorithm is superior in clustering performance. It reveals that the interval weighted clustering can act as an optimization operator on the basis of the traditional numerical weighted clustering, and the effects of interval weight perturbation on clustering performance can be decreased.

33 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: Overall, the experimental analysis shows that the equal distribution of gray watermark over RGB components with PSO optimized scaling factors provides significant improvement in the quality of watermarked image and thequality of retrieved watermark even from the distorted water marked image.
Abstract: The present study is conducted in two phases. In the first phase we analyze the different aspects of gray image watermarking in a colored host. Robustness and imperceptibility are used as analysis parameters. The approaches explored and compared in this study are — watermark embedding with any one of the three RGB (Red-Green-Blue) components (single channel embedding), multichannel watermark embedding (same watermark with all channels) and multichannel embedding with equally segmented watermark. SVD (Singular Value Decomposition) is used to calculate the singular values of host image and then appropriate scaling factor isused to embed the watermark and the watermarked image is subjected to different attacks. To secure the watermark from an unauthorized access Arnold transform is implemented. From the simulation results it is observed that segmented watermark approach is better than the other two approaches in terms of both robustness and imperceptibility. In the second phase, change of robustness and imperceptibility is studied with the change of scaling factor for which PSO (Particle swarm optimization) is employed to determine the optimal values of scaling factor. The results here indicate that the use of different scaling factors (optimal) for each RGB component provides better result in comparison to a single (optimal) scaling factor in segmented multichannel approach. Overall, the experimental analysis shows that the equal distribution of gray watermark over RGB components with PSO optimized scaling factors provides significant improvement in the quality of watermarked image and the quality of retrieved watermark even from the distorted watermarked image.

14 citations

Journal Article
TL;DR: An entropy- based method is developed for selection of DWT coefficients that provides an adaptive way for determining the number of watermarked coefficients and watermarking factor at each level ofDWT decomposition.
Abstract: A new approach for non- blind watermarking of still gray level images in the wavelet domain. The method uses the Human Visual System (HVS) characteristic, and an innovative entropy based approach to create an efficient watermarking scheme is presented in this paper. It decomposes original image in DWT domain in to three hierarchical levels and watermarks it with a logo image, which is scrambled through a well- known PN- sequence. An entropy- based method is developed for selection of DWT coefficients that provides an adaptive way for determining the number of watermarked coefficients and watermarking factor at each level of DWT decomposition. This approach shows an excellent resistance against almost all the attacks known in the watermarking literature. The detection results of the method reveal better resistance in comparison to the existance methods.With simple modifications, the method can be used for color images and in real time systems.

14 citations

Proceedings ArticleDOI
22 Dec 2011
TL;DR: The proposed method demonstrates that color image watermarking is possible without affecting its visual quality and also allows for reasonable compromise between robustness and invisibility of watermarks.
Abstract: In this paper we propose a new invisible, non-blind digital watermarking scheme for color images based on singular vector domain (SVD). In this new approach, same or different watermarks can be embedded into the three color channels (R, G, B) of the host image in order to increase the robustness of the watermark. The proposed method demonstrates that color image watermarking is possible without affecting its visual quality and also allows for reasonable compromise between robustness and invisibility of watermarks. This research, based on our earlier work, consists of embedding a gray watermark into a gray host and a color watermark into a color host based on the singular vector domain. The main feature of our scheme is to use all channels of RGB color space to embed watermarks. Hence we are embedding maximum amount of watermark and also make it more secure. The other feature of our scheme is that it can be used for a variety of applications since we can perform single channel embedding to multichannel embedding. We then show our simulation results for one channel, two channel and three channel and also results for how it behaves under various attacks.

10 citations