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

Euler number is a fundamental topological feature of an image. In this paper, a novel algorithm for computing the Euler number of a binary image based on divide-and-conquer paradigm, is proposed, which outperforms significantly the conventional techniques used in image processing tools. The algorithm can be easily parallelized for computing the Euler number of an N/spl times/N image in O(N) time, with O(N) processors. Using a simple architecture, the proposed method can be implemented as a special purpose VLSI chip.

A fast algorithm for computing the Euler number of an image and its VLSI implementation

A Mahalanobis distance measure is used to identify a query image among plural images in a database. The measure may be used to rank the similarity of one or more images to the query image. A varance-covariance matrix is calculated for all images in the database. The variance-covariance matrix is used to calculate the Mahalanobis distance between the query image and one or more images in the database. A range tree may be used to identify likely image candidates for performing the Mahalanobis distance measurement.

Image retrieval using distance measure

A pipeline architecture for computing the Euler number of a binary image

A novel method for image thresholding using interval type-2 fuzzy set and Bat algorithm

This paper describes two examples of real-life applications of texture segmentation using M-band wavelets. In the rst part of the paper, an ecien t and computationally fast method for segmenting text and graphics part of a document image based on textural cues is presented. It is logical to assume that the graphics part has dieren t textural properties than the non-graphics (text) part. So, this is basically a two-class texture segmentation problem. The second part of the paper describes a segmentation scheme for another real-life data such as remotely sensed image. Dieren t quasi-homogeneous regions in the image can be treated to have dieren t texture properties. Based on this assumption the multi-class texture segmentation scheme is applied for this purpose.

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

Malay K. Kundu

Papers

A fast algorithm for computing the Euler number of an image and its VLSI implementation

Image retrieval using distance measure

A pipeline architecture for computing the Euler number of a binary image

A novel method for image thresholding using interval type-2 fuzzy set and Bat algorithm

M-band wavelets: application to texture segmentation for real life image analysis