A new feature selection approach with binary exponential henry gas solubility optimization and hybrid data transformation methods.

In the common classification practices, feature selection is an important aspect that highly impacts the computation efficacy of the model, while implementing complex computer vision tasks. The metaheuristic optimization algorithms gain popularity to obtain optimal feature subset. However, the feature selection using metaheuristics suffers from two common stability problems, namely premature convergence and slow convergence rate.

A new firefly algorithm-based superpixel clustering method for vehicle segmentation.

The vehicle segmentation in the images of a crowded and unstructured road traffic, having inconsistent driving patterns and vivid attributes like colour, shapes, and size, is a complex task. For the same, this paper presents a new firefly algorithm-based superpixel clustering method for vehicle segmentation. The proposed method introduces a modified firefly algorithm by incorporating the best solution for enhancing the exploitation behaviour and solution precision.

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets.

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods.

Feature selection and classification of leukocytes using random forest.

In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space.

Automated microscopic image analysis for leukocytes identification: a survey.

Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification.