C. A. Murthy

Unsupervised feature selection using feature similarity

In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.

In search of optimal clusters using genetic algorithms

Genetic Algorithms (GAs) are generally portrayed as search procedures which can optimize functions based on a limited sample of function values. In this paper, GAs have been used in an attempt to optimize a specified objective function related to a clustering problem. Several experiments on synthetic and real life data sets show the utility of the proposed method. K-Means is one of the most popular methods adopted to solve the clustering problem. Analysis of the experimental results shows that the proposed method may improve the final output of K-Means where an improvement is possible.

Hue-preserving color image enhancement without gamut problem

The first step in many techniques for processing intensity and saturation in color images keeping hue unaltered is the transformation of the image data from RGB space to other color spaces such as LHS, HSI, YIQ, HSV, etc. Transforming from one space to another and processing in these spaces usually generate a gamut problem, i.e., the values of the variables may not be in their respective intervals. We study enhancement techniques for color images theoretically in a generalized setup. A principle is suggested to make the transformations gamut-problem free. Using the same principle, a class of hue-preserving, contrast-enhancing transformations is proposed; they generalize existing grey scale contrast intensification techniques to color images. These transformations are also seen to bypass the above mentioned color coordinate transformations for image enhancement. The developed principle is used to generalize the histogram equalization scheme for grey scale images to color images.

Genetic algorithm with elitist model and its convergence

In this article, the genetic algorithm with elitist model (EGA) is modeled as a finite state Markov chain. A state in the Markov chain denotes a population together with a potential string. Proof for the convergence of an EGA to the best chromosome (string), among all possible chromosomes, is provided here. Mutation operation has been found to be essential for convergence. It has been shown that an EGA converges to the global optimal solution with any choice of initial population.

Density-based multiscale data condensation

A problem gaining interest in pattern recognition applied to data mining is that of selecting a small representative subset from a very large data set. In this article, a nonparametric data reduction scheme is suggested. It attempts to represent the density underlying the data. The algorithm selects representative points in a multiscale fashion which is novel from existing density-based approaches. The accuracy of representation by the condensed set is measured in terms of the error in density estimates of the original and reduced sets. Experimental studies on several real life data sets show that the multiscale approach is superior to several related condensation methods both in terms of condensation ratio and estimation error. The condensed set obtained was also experimentally shown to be effective for some important data mining tasks like classification, clustering, and rule generation on large data sets. Moreover, it is empirically found that the algorithm is efficient in terms of sample complexity.

Thresholding in edge detection: a statistical approach

Many edge detectors are available in image processing literature where the choices of input parameters are to be made by the user. Most of the time, such choices are made on an ad-hoc basis. In this article, an edge detector is proposed where thresholding is performed using statistical principles. Local standardization of thresholds for each individual pixel (local thresholding), which depends upon the statistical variability of the gradient vector at that pixel, is done. Such a standardized statistic based on the gradient vector at each pixel is used to determine the eligibility of the pixel to be an edge pixel. The results obtained from the proposed method are found to be comparable to those from many well-known edge detectors. However, the values of the input parameters providing the appreciable results in the proposed detector are found to be more stable than other edge detectors and possess statistical interpretation.

A probabilistic active support vector learning algorithm

The paper describes a probabilistic active learning strategy for support vector machine (SVM) design in large data applications. The learning strategy is motivated by the statistical query model. While most existing methods of active SVM learning query for points based on their proximity to the current separating hyperplane, the proposed method queries for a set of points according to a distribution as determined by the current separating hyperplane and a newly defined concept of an adaptive confidence factor. This enables the algorithm to have more robust and efficient learning capabilities. The confidence factor is estimated from local information using the k nearest neighbor principle. The effectiveness of the method is demonstrated on real-life data sets both in terms of generalization performance, query complexity, and training time.

Pattern classification with genetic algorithms

A method is proposed for finding decision boundaries, approximated by piecewise linear segments, for the classification of patterns in R 2 , using an elitist model of a genetic algorithm. It involves the generation and placement of a set of lines (represented by strings) in the feature space that yields minimum misclassification. The effectiveness of the algorithm is demonstrated, for different parameter values, on both artificial data and speech data having non-linear class boundaries. Its comparison with the k-NN classifier is also made.

Technique for fractal image compression using genetic algorithm

A new method for fractal image compression is proposed using genetic algorithm (GA) with an elitist model. The self transformation property of images is assumed and exploited in the fractal image compression technique. The technique described utilizes the GA, which greatly decreases the search space for finding the self similarities in the given image. This article presents theory, implementation, and an analytical study of the proposed method along with a simple classification scheme. A comparison with other fractal-based image compression methods is also reported.

Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique

Abstract The problem of histogram sharpening and thresholding by minimising greylevel fuzziness is considered. The earlier work on the said problem consists only of algorithms without mathematical justification of the findings. For example, the choices of appropriate membership function and the optimum value of its window size (band width) for detecting thresholds were made experimentally with iterative manner. The present work provides a complete theoretical formulation of the same and establishes the criteria regarding the choices of membership function and its window size (band width). The variation in membership function is seen to be restricted by bound functions, thus enabling the method of segmentation more flexible but effective. Finally, the method can be viewed as a weighted moving average technique, greyness ambiguity being the weights.