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

This paper proposes a new image retrieval scheme using visually significant features. Clusters of points around significant curvature regions (high, medium, weak type) are extracted to obtain a representative image. Illumination, viewpoint invariant color features are computed from those points for evaluating similarity between images. Relative importance of the features is evaluated using a fuzzy entropy based measure computed from relevant and irrelevant set of the retrieved images marked by the users. The performance of the system is tested using different set of examples from general purpose image database. Robustness of the system has also been shown when the images have undergone different transformations.

Image retrieval with visually prominent features using fuzzy set theoretic evaluation

http://www.isical.ac.in/~malay/Papers/Pattern%20Recognition%20Letters%2012%20(1991)%20491-496.pdf

A parallel graytone thinning algorithm (PGTA)

We propose an approach for texture feature extraction based on M-band wavelet packet frames. The features so extracted are used for segmentation of multi texture images. Standard dyadic wavelets are not suitable for the analysis of high frequency signals with relatively narrow bandwidth and also are not translation invariant. Also, since most significant information of a texture often lies in the intermediate frequency bands, the present work employs an overcomplete wavelet decomposition scheme called discrete M-band wavelet packet frame (DM-bWPF), which yields improved segmentation accuracies. Wavelet packets represent a generalization of the method of multiresolution decomposition and comprise all possible combinations of subband tree decomposition. We propose a computationally efficient search procedure to find the optimal basis based on some maximum criterion of textural measures derived from the statistical parameters of each of the subbands, to locate dominant information in each subband (frequency channel) and decide further decomposition.

Adaptive basis selection for multi texture segmentation by M-band wavelet packet frames

The present article describes a image segmenta- tion technique using M -band wavelet packet frames features. Those wavelet features are evaluated and selected using an efficient neuro-fuzzy feature evaluation techniqu. Both the feature extraction and neuro-fuzzy feature evaluation processes are of unsupervised type. They do not require the knowledge of number and distribution of classes corresponding to various landcovers in remotely sensed images. The effectiveness of the methodology is demonstrated on IRS-1A and SPOT images. Copyright c ∞ 2007 Yang's Scientific Research Institute, LLC. All rights reserved. Index Terms—M -band wavelet packet frames, adaptive basis selection, fuzzy feature evaluation index, neural networks, re- motely sensed image

Image Segmentation Using Wavelet Packet Frames and Neuro-fuzzy Tools

Reliable corner detection is an important task in determining shape of different regions in an image. To detect corners in a gray level image under imprecise information, an algorithm based on fuzzy set theoretic model is proposed. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, etc., usually result in missing of significant curvature junctions (corners). Fuzzy set theory based modeling is well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model image properties in fuzzy frame work for reliable decision making. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm.

https://www.isical.ac.in/~malay/Papers/APPLIED_SOFT_COMP_08N.pdf

Malay K. Kundu

Papers

Image retrieval with visually prominent features using fuzzy set theoretic evaluation

A parallel graytone thinning algorithm (PGTA)

Adaptive basis selection for multi texture segmentation by M-band wavelet packet frames

Image Segmentation Using Wavelet Packet Frames and Neuro-fuzzy Tools

Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach