Average Entropy: A New Uncertainty Measure with Application to Image Segmentation

Comparison of two-lifetime models of solid-state lighting based on sup-entropy

“…On the other hand, some authors used consistent measures of information like Kullback-Leibler divergence as a measure of discrimination between two life-time models [9, 10, 11] and others tried to modify or change the primary definition of entropy. Among all, we mention the cumulative residual entropy [12], the average entropy [13] and in particular, the sup-entropy [14]. The sup-entropy has been recently used as a quantifier of the efficiency of the censored sample for several survival distributions such as exponential distribution [15, 16] , Pareto [17] , Weibull [18, 19].…”

Estimating the Income Distribution of Some Muslim Countries Based on Entropy Measures

“…Consequently, Shannon entropy became important in quantifying randomness present in several fields such as financial analysis (Sharpe, 1985), data compression (Salomon, 2008), statistics (Kullback, 1959), and information theory and data transmission (Cover & Thomas, 1991). Recently, modified versions of this measure have been utilized in lifetime studies (Kittaneh & Akbar, 2016) and image processing (Kittaneh, Khan, Akbar, & Bayoud, 2016). Jaynes (1957) was the first to introduce the principle of maximum entropy and the induced maximum entropy distribution.…”

A tutorial review on entropy-based handcrafted feature extraction for information fusion

“…An alignment method for inverse synthetic aperture radar image formation, a hyper-spectral image segmentation using Renyi entropy and a fuzzy image texture analysis and classification approach are, respectively, the topics of papers [80] , [64] and [58] . Importantly, a new uncertainty measure for image segmentation called average entropy is defined by the authors of paper [43] .…”