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

Genetic algorithms (GAs) represent a class of highly parallel adaptive search processes for solving a wide range of optimization and machine learning problems. The authors attempt to demonstrate the suitability of GAs in the automatic selection of the image enhancement operator for an unknown image. The problem is to select automatically an optimum set of 12 parameter values of a generalized enhancement function that maximizes some fitness function. The algorithm used both spatial and grayness ambiguity measures as the fitness value. A multiple point genetic cross-over operation has been used for better convergence. The algorithm does not need iterative visual interaction and prior knowledge of image statistics to select the appropriate enhancement function. Convergence of the algorithm was experimentally verified. >

Image enhancement incorporating fuzzy fitness function in genetic algorithms

In this article, a novel content based image retrieval (CBIR) system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result, a fuzzy relevance feedback mechanism (F-RFM) is also implemented. Fuzzy entropy based feature evaluation mechanism is used for automatic computation of revised feature's importance and similarity distance at the end of each iteration. Experimental results on a large image database demonstrate the efficiency and effectiveness of the proposed CBIR system in the image retrieval paradigm

https://www.isical.ac.in/%7Emalay/Papers/Conf/PerMIn12__print%20copy.pdf

Interactive content based image retrieval using ripplet transform and fuzzy relevance feedback

Content-Based Medical Image Retrieval (CBMIR) is an important research field in the context of medical data management. In this paper we propose a novel CBMIR system for the automatic retrieval of radiographic images. Our approach employs a Convolutional Neural Network (CNN) to obtain high-level image representations that enable a coarse retrieval of images that are in correspondence to a query image. The retrieved set of images is refined via a non-parametric estimation of putative classes for the query image, which are used to filter out potential outliers in favour of more relevant images belonging to those classes. The refined set of images is finally re-ranked using Edge Histogram Descriptor, i.e. a low-level edge-based image descriptor that allows to capture finer similarities between the retrieved set of images and the query image. To improve the computational efficiency of the system, we employ dimensionality reduction via Principal Component Analysis (PCA). Experiments were carried out to evaluate the effectiveness of the proposed system on medical data from the “Image Retrieval in Medical Applications” (IRMA) benchmark database. The obtained results show the effectiveness of the proposed CBMIR system in the field of medical image retrieval.

/pdf/an-efficient-radiographic-image-retrieval-system-using-odaldavtsc.pdf

An efficient radiographic Image Retrieval system using Convolutional Neural Network

Perception and Machine Intelligence

Automatic Fingerprint Identification Systems (AFIS) are usually based on minutiae matching. Minutiae are the terminations and bifurcations of the ridge lines in a fingerprint image. Detection of ridge lines from a noisy gray-scale fingerprint image is a challenging task. In this work, a novel combinatorial approach is proposed for classifying each pixel into one of the three classes (crest, valley, and slope) based on its gray-scale topographical relationship with its neighbors. A two-pass algorithm is developed for the pixel classification scheme. The ridge lines are then detected as formed by the thinned version of the crest pixels. The algorithm is robust, performs very well in the presence of noise, and has minimal dependence on thresholding. It has been tested on several fingerprint images in the NIST Special Database 14 and NIST Special Database 4, and is observed to produce good results both in terms of quality of solutions and CPU time.

Malay K. Kundu

Papers

Image enhancement incorporating fuzzy fitness function in genetic algorithms

Interactive content based image retrieval using ripplet transform and fuzzy relevance feedback

An efficient radiographic Image Retrieval system using Convolutional Neural Network

Perception and Machine Intelligence

Combinatorial Classification of Pixels for Ridge Extraction in a Gray-Scale Fingerprint Image.