Earticle

Multimedia security is extremely significant concern for the internet technology because of the ease of the duplication, distribution and manipulation of the multimedia data. The digital watermarking is a field of information hiding which hide the crucial information in the original data for protection illegal duplication and distribution of multimedia data. This paper presents a survey on the existing digital image watermarking techniques. The results of various digital image watermarking techniques have been compared on the basis of outputs. In the digital watermarking the secret information are implanted into the original data for protecting the ownership rights of the multimedia data. The image watermarking techniques may divide on the basis of domain like spatial domain or transform domain or on the basis of wavelets. The spatial domain techniques directly work on the pixels and the frequency domain works on the transform coefficients of the image. This survey elaborates the most important methods of spatial domain and transform domain and focuses the merits and demerits of these techniques.

In this work, we have presented a hierarchical scheme for classifying music data. Instead of dealing with large variety of features, proposed scheme relies on MFCC and its variants which are introduced at the different stages to satisfy the need. At the top level music is classified as song (music with voice) and instrumental (music without voice) based on MFCC. Subsequently, instrumental signals and songs are classified based on instrument type and genres respectively. Hierarchical approach has been followed for such detailed categorization. Using two-stage process, instrumental signals are identified as one of the four types namely, string, woodwind, percussion or keyboard. Wavelet and MFCC based features are used for this purpose. For song classification, at first level signals are categorized as classical or non-classical(popular) ones by capturing the MFCC pattern present in the high sub-band of wavelet decomposed signal. At second level, we consider the task of further classification of popular songs into various genres like Pop, Jazz, Bhangra (an Indian genre) based on amplitude variation pattern. RANSAC has been utilized as the classifier at all stages. Experimental result indicates the effectiveness of the proposed schemes.

In recent years, semi-supervised learning has been a hot research topic in machine learn-ing area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each exam-ple is represented by two or more redundantly sufficient sets of features (views) and addi-tionally these views are independent given the class. To improve the performance and ap-plicability of co-training, ensemble learning, such as bagging and random subspace has been used along with co-training. In this work, we propose to use the rough set based ensem-ble learning method with co-training algorithm (RSCO). Inherited the inherent characteris-tics of rough set, ensemble learning is expected to meet both the diversity and accuracy re-quirement. Finally experimental results on the UCI data sets demonstrate the promising per-formance of RSCO.

Organic Light Emitting Diode (OLED) displays have matured into current smartphones. How to prolong the lifetime of displays while preserving the display quality becomes a primary issue. In this paper, we propose a low image distortion constrained power-saving approach for OLED displays based on gamma correction and saturation scaling. We first investigate the impact of gamma correction and saturation scaling on the power of emissive displays. The results show that changing the gamma and saturation value can obtain lower display power consumption when original image color maps to another one. Thus, we integrate the gamma correction and the saturation scaling into a new low-power approach for OLED displays. However, low gamma and high saturation lead to distortion on displaying. To guarantee user experience in this paper, the CIEDE2000 color difference formula and the Mean Structural Similarity Index (MSSIM) are used to evaluate the effectiveness of our approach. The results show that our approach saves up significant display power with high image quality.

Complementary filters coupled with MEMS IMU are preferred in applications where computational simplicity, low power and low cost is of prime importance. Such algorithms are equipped with fixed filter’s gain, however improvements can be realized by changing the filter’s gain as per the dynamic situation experienced by the platform. This paper is intended to evaluate the performance of conventional fixed gain complementary algorithm against adoptive gain complementary filter for attitude estimation using MEMS IMU as a test subject. As only IMU (Inertial Measurement Unit) has been exploited without using any aided sensory system, so the mandate is limited to evaluate performance of these algorithms on the basis of Euler angles roll and pitch estimation. Significant performance improvement is observed by varying filter gain in accordance with dynamic situation experienced by the system.

This paper presents a general framework for seamlessly combining multiple low cost and inaccurate estimated segmentation maps (with an arbitrary number of regions) of the same scene to achieve a final improved segmentation. The proposed fusion model is derived from the well-known precision-recall criterion, specially dedicated to the specific clustering problem of any spatially indexed data and which is also efficient and widely used in the vision community for evaluating both a region-based segmentation and the quality of contours produced by this segmentation map compared to one or multiple ground-truth segmentations of the same image. The proposed combination framework is here specifically designed to be robust with respect to outlier segmentations (that appear to be inconsistent with the remainder of the segmentation ensemble) and includes an explicit internal regularization factor reflecting the inherent ill-posed nature of the segmentation problem. We propose also a hierarchical and efficient way to optimize the consensus energy function related to this fusion model that exploits a simple and deterministic iterative relaxation strategy combining the different segments or individual regions belonging to the segmentation ensemble in the final solution. The experimental results on the Berkeley database with manual ground truth segmentations show the effectiveness of our combination model.

As one of the most important video compression technologies, inter prediction coding is highly efficient in reducing the temporal redundancy of video sequence. However, complicated inter prediction for the latest High Efficiency Video Coding standard (HEVC) brings high computational complexity and seriously restricts the encoding speed. In this paper, a fast inter prediction algorithm based on Rate-Distortion (RD) cost is proposed to improve inter prediction of HEVC. First, the splitting of Largest Coding Unit (LCU) is determined according to the RD costs with best Coding Unit (CU) size being 64x64 in the reference picture. Then, for other CUs in lower depths, the comparable RD costs are selected from encoded CUs in the same depth at the same Coding Tree Unit (CTU) based on the local homogeneity in spatial domain. By comparing the RD cost of current CU with its corresponding RD threshold, the splitting is terminated in advance. In this way, the proposed fast inter prediction algorithm can avoid the traversal of all CUs in the coding tree structure and improve the encoding speed. Experimental results show that the algorithm can save about 30% encoding time on the basis of ensuring visual quality and compression ratio of videos. Therefore, the computational complexity can be reduced greatly.

Image Registration is the process of aligning two or more images of the same scene taken from different viewpoints, at different times, from different sensors. Image registration aligns two images, i.e., base image and reference image, geometrically. There are different approaches of image registration and these approaches are categorized according to their nature that is areas based and feature based. These approaches are also categorized according to the four simple steps of image registration procedure: feature detection, feature matching, function mapping and transformation and re-sampling. Advantages and disadvantages of different methods are discussed in the paper. The main aim of this paper is to provide the knowledge of different image registrations methods used in different application area.

Density-based spatial clustering of applications with noise (DBSCAN) is a base algorithm for density based clustering. It can find out the clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers. However, it fails to handle the local density variation that exists within the cluster. Thus, a good clustering method should allow a significant density variation within the cluster because, if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper an enhancement of DBSCAN algorithm is proposed, which detects the clusters of different shapes, sizes that differ in local density. We introduce new algorithm Dynamic Method DBSCAN (DMDBSCAN). It selects several values of the radius of a number of objects (Eps) for different densities according to a k-dist plot. For each value of Eps, DBSCAN algorithm is adopted in order to make sure that all the clusters with respect to the corresponding density are clustered. For the next process, the points that have been clustered are ignored, which avoids marking both denser areas and sparser ones as one cluster. Experimental results are obtained from artificial data sets and UCI real data sets. The final results show that our algorithm get a good results with respect to the original DBSCAN and DVBSCAN algorithms.