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

The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.

Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine

This paper proposes a transform domain data-hiding scheme for quality access control of images. The original image is decomposed into tiles by applying n-level lifting-based discrete wavelet transformation (DWT). A binary watermark image (external information) is spatially dispersed using the sequence of number generated by a secret key. The encoded watermark bits are then embedded into all DWT-coefficients of nth-level and only in the high-high (HH) coefficients of the subsequent levels using dither modulation (DM) but without complete self-noise suppression. It is well known that due to insertion of external information, there will be degradation in visual quality of the host image (cover). The degree of deterioration depends on the amount of external data insertion as well as step size used for DM. If this insertion process is reverted, better quality of images can be accessed. To achieve that goal, watermark bits are detected using minimum distance decoder and the remaining self-noise due to information embedding is suppressed to provide better quality of image. The simulation results have shown the validity of this claim.

Quantization Based Data Hiding Scheme for Efficient Quality Access Control of Images Using DWT via Lifting

While surveillance video is the biggest source of unstructured Big Data today, the emergence of high-efficiency video coding (HEVC) standard is poised to have a huge role in lowering the costs associated with transmission and storage Among the benefits of HEVC over the legacy MPEG-4 Advanced Video Coding (AVC), is a staggering 40 percent or more bitrate reduction at the same visual quality Given the bandwidth limitations, video data are compressed essentially by removing spatial and temporal correlations that exist in its uncompressed form This causes compressed data, which are already de-correlated, to serve as a vital resource for machine learning with significantly fewer samples for training In this paper, an efficient approach to foreground extraction/segmentation is proposed using novel spatio-temporal de-correlated block features extracted directly from the HEVC compressed video Most related techniques, in contrast, work on uncompressed images claiming significant storage and computational resources not only for the decoding process prior to initialization but also for the feature selection/extraction and background modeling stage following it The proposed approach has been qualitatively and quantitatively evaluated against several other state-of-the-art methods

Efficient Foreground Extraction From HEVC Compressed Video for Application to Real-Time Analysis of Surveillance ‘Big’ Data

This paper investigates the scope of wavelets for performance improvement in spread spectrum image watermarking. Performance of a digital image watermarking algorithm, in general, is determined by the visual invisibility of the hidden data (imperceptibility), reliability in the detection of the hidden information after various common and deliberate signal processing operations (robustness) applied on the watermarked signals and the amount of data to be hidden (payload) without affecting the imperceptibility and robustness properties. In this paper, we propose a few spread spectrum (SS) image watermarking schemes using discrete wavelet transform (DWT), biorthogonal DWT and M-band wavelets coupled with various modulation, multiplexing and signaling techniques. The performance of the watermarking methods are also reported along with the relative merits and demerits.

https://www.isical.ac.in/~malay/Papers/IJWMIP_2011.pdf

Performance improvement in spread spectrum image watermarking using wavelets

A new combinatorial characterization of a gray-tone image called Euler Vector is proposed. The Euler number of a binary image is a well-known topological feature, which remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. The Euler vector comprises a 4-tuple, where each element is an integer representing the Euler number of the partial binary image formed by the gray-code representation of the four most significant bit planes of the gray-tone image. Computation of Euler vector requires only integer and Boolean operations. The Euler vector is experimentally observed to be robust against noise and compression. For efficient image indexing, storage and retrieval from an image database using this vector, a bucket searching technique based on a simple modification of Kd-tree, is employed successfully. The Euler vector can also be used to perform an efficient four-dimensional range query. The set of retrieved images are finally ranked on the basis of Mahalanobis distance measure. Experiments are performed on the COIL database and results are reported. The retrieval success can be improved significantly by augmenting the Euler vector by a few additional simple shape features. Since Euler vector can be computed very fast, the proposed technique is likely to find many applications to content-based image retrieval.

https://www.isical.ac.in/~malay/Papers/smc_2005.pdf

Malay K. Kundu

Papers

Video shot boundary detection using multiscale geometric analysis of nsct and least squares support vector machine

Quantization Based Data Hiding Scheme for Efficient Quality Access Control of Images Using DWT via Lifting

Efficient Foreground Extraction From HEVC Compressed Video for Application to Real-Time Analysis of Surveillance ‘Big’ Data

Performance improvement in spread spectrum image watermarking using wavelets

Euler vector for search and retrieval of gray-tone images