No abstract

This paper addresses the concern of video piracy and also resolves ownership rights. Piracy entails utilizing, modifying and sharing the data without owner concern. Identifying the owner of the piracy and also the legal owner is difficult. For resolving these problems, this paper employs the prominent technique like watermarking with legal data base creation and SVD in wavelet domain. In addition to that, master key frame selection concept is used for identifying the key frames. Here the problem is resolved by two approaches. That is visible and invisible watermark to the authorized data. The watermark may be legal information about the owner like photo, thumb impression, signature of the owner and legal id. The legal id is the visible watermark and photo, thumb impression or signature of the owner is inserted as an invisible watermark. This methodology resists several video and image processing attacks. The experimental results show that the algorithm produces good perceptual quality and robustness to the watermarked video as well as watermark.

Image fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.Keywords: region based energy method • discrete wavelet transform • discrete fast curvelet transform • root mean square error © Versita sp. z o.o.

Abstract---In this paper, we propose the MIML (MultiInstance Multi-Label learning)

This paper presents a novel image annotation framework for domains with large numbers of images. Automatic image annotation is such a domain, by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision technique is used in image retrieval system to organize and locate images of interest from a database. Many techniques have been proposed for image annotation in the last decade that has given reasonable performance on standard datasets In this work, we propose a new model for image annotation known as JSVM which treats annotation as a retrieval problem. In this work, we introduce an JSVM model for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low level image features and a simple combination of basic distances using JEC to find the nearest neighbors of a given image; the keywords are then assigned using SVM approach which aims to explore the combination of three different methods. First, the initial annotation of the data using flat wise and axis wise methods, and that takes the hierarchy into consideration by classifying consecutively its instances through position wise method. Finally, we make use of pair wise majority voting between methods by simply summing strings in order to produce a final annotation. The result of the proposed technique shows that this technique outperforms the current state of art methods on the standard datasets.