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

Content-Based Image Retrieval (CBIR) is an important problem in the domain of digital data management. There is indeed a growing availability of images, but unfortunately the traditional metadata-based search systems are unable to properly exploit their visual information content. In this article we introduce a novel CBIR scheme that abstracts each image in the database in terms of statistical features computed using the Multi-scale Geometric Analysis (MGA) of Non-subsampled Contourlet Transform (NSCT). Noise resilience is one of the main advantages of this feature representation. To improve the retrieval performance and reduce the semantic gap, our system incorporates a Relevance Feedback (RF) mechanism that uses a graph-theoretic approach to rank the images in accordance with the user's feedback. First, a graph of images is constructed with edges reflecting the similarity of pairs of images with respect to the proposed feature representation. Then, images are ranked at each feedback round in terms of the probability that a random walk on this graph reaches an image tagged as relevant by the user before hitting a non-relevant one. Experimental analyses on three different databases show the effectiveness of our algorithm compared to state-of-the-art approaches in particular when the images are corrupted with different types of noise.

https://www.isical.ac.in/~malay/Papers/Knowledge%20Based%20System_manuscript_v2.0.pdf

A graph-based relevance feedback mechanism in content-based image retrieval

The paper proposes a blind spread spectrum watermarking scheme where watermark information is embedded redundantly in the multilevel wavelet coefficients of the cover image. It has been shown that for a given embedding distortion, data embedding in LL and HH bands offers higher resiliency through better spectrum spreading compared to LH and HL band embedding, although the security of the hidden data is better in the later case. High resiliency of the scheme is supported by the good visual quality of the watermark images extracted from the several distorted watermarked images. Data hiding capacity is increased using code division multiple access (CDMA) technique and the mutual information values for the different watermark images are able to detect the degree of distortion already occurred in the watermarked image.

http://www.isical.ac.in/~malay/Papers/Conf/ICIP'04_1595.pdf

A blind CDMA image watermarking scheme in wavelet domain

An adaptive approach to unsupervised texture segmentation using M -Band wavelet transform

Disclosed is a method and apparatus to provide for fingerprint minutiae matching using scoring techniques. A plurality of minutiae in a fingerprint image is defined. A score associated with each minutia corresponding to the validity of each minutia is estimated. The fingerprint image is then matched against one or more sample fingerprint images utilizing a partial point set pattern matching (PSPM) algorithm.

Fingerprint minutiae matching using scoring techniques

The paper proposes a digital image watermarking scheme that selects regions for data embedding based on information measure. Two valued kernels of Hadamard transformation cause smaller image information change during embedding compared to other transform domains such as DCT (discrete cosine transform), DFT (discrete Fourier transform), Fourier–Mellin and wavelet-based embedding. Moreover, the usage of Hadamard transform as signal decomposition tool offers advantages in terms of simpler implementation, low computation cost and high resiliency at low quality compression considering both JPEG and JPEG 2000 framework. Compression resiliency is further improved using adaptive negative modulation. These facts are validated comparing the performance with some other existing watermarking schemes as well as DCT domain implementation of the proposed scheme.

https://www.isical.ac.in/~malay/Papers/ijec_10.pdf

Malay K. Kundu

Papers

A graph-based relevance feedback mechanism in content-based image retrieval

A blind CDMA image watermarking scheme in wavelet domain

An adaptive approach to unsupervised texture segmentation using M -Band wavelet transform

Fingerprint minutiae matching using scoring techniques

DHT domain digital watermarking with low loss in image informations