There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A review on image segmentation techniques

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Review: Digital image steganography: Survey and analysis of current methods

Genetic algorithm-based clustering technique

A new technique of digital watermarking for gray-level images is reported in this paper. Digital watermarking/hiding has emerged as a new area of research in connection to intellectual property (IP) protection of images. The redundancy in image representation can be exploited successfully to hide some characteristic information in images, without noticeable distortion. Hidden information after extraction, can be used to protect copyright. This article deals with a block-based technique for inserting a copyright mark (invisible logo) into a gray-level image. The robustness of the proposed method has been tested against non-destructive attacks like mean and median filtering.

https://www.isical.ac.in/~malay/Papers/Conf/TENCON_WM_2003.pdf

Digital watermarking using homogeneity in image

Genetic Algorithm for Improvement in Detection of Hidden Data in Digital Images

The widely used feature representation scheme for magnetic resonance (MR) image classification based on low-frequency subband (LFS) coefficients of wavelet transform (WT) is ineffective in presence of common MR imaging (MRI) artifacts (small rotation, low dynamic range etc.). The directional information present in the high-frequency subbands (HFSs) can be used to improve the performance. Moreover, little attention has been paid to the newly developed multiscale geometric analysis (MGA) tools (curvelet, contourlet, and ripplet etc.) in classifying brain MR images. In this paper, we compare various multiresolution analysis (MRA)/MGA transforms, such as traditional WT, curvelet, contourlet and ripplet, for brain MR image classification. Both the LFS and the high-frequency subbands (HFSs) are used to construct image representative feature vector invariant to common MRI artifacts. The investigations include the effect of different decomposition levels and filters on classification performance. By comparing results, we give the best candidate for classifying brain MR images in presence of common artifacts.

/pdf/robust-classification-of-mr-brain-images-based-on-multiscale-1kos708s3b.pdf

Robust Classification of MR Brain Images Based on Multiscale Geometric Analysis

Due to the advancement in digital wireless communication 3rd/future generation mobile communication system expects to offer multimedia services and applications with negotiated end-to-end quality assurance. Hence devising a simple and effective billing system related to the quality of services (QoS) supplied becomes essential to the service providers. The paper proposes a low-cost spread spectrum (SS) watermarking scheme in multimedia data where the estimation of the tracing watermark at mobile station (MS) end would blindly assess the quality of services (QoS). Furthermore, the quality of tracing watermarks obtained from multipath can be used to combat the fading effect using di-

/pdf/watermarking-scheme-for-blind-quality-assessment-in-2zr7p61i8r.pdf

Malay K. Kundu

Papers

Digital watermarking using homogeneity in image

Genetic Algorithm for Improvement in Detection of Hidden Data in Digital Images

Robust Classification of MR Brain Images Based on Multiscale Geometric Analysis

Watermarking Scheme for Blind Quality Assessment in Multimedia Mobile Communication Services.

Robust texture classification using wavelet frames