Advances in Image Feature Extraction and Selection

Dear Colleagues,

Today, humanity has more data than ever. It is crucial to work with uncovering actionable, rather than interesting data—knowing the difference between interesting data and useful data. amongst the important aspects in machine learning are “feature selection” and “feature extraction”.

A universal problem of intelligent (learning) approaches is where to focus their attention. It is very crucial to understand “What are the aspects of the problem at hand are important/necessary to solve it?”, i.e., discriminate between the relevant and irrelevant parts of imaging data.

The problem is selecting some subsets of a learning algorithm’s input variables upon which it should focus its attention, while ignoring the rest—in other words, dimensionality reduction, which is something that we, as Humans, constantly do.

Feature selection becomes necessary, especially when dealing with a large number of data (dimensionality reduction), improving, significantly, a learning algorithm’s performance.

In real-world applications, this is usually not possible: For most problems, it is computationally intractable to search the whole space of possible feature subsets; one usually has to settle for approximations of the optimal subset; most of the research in this area is devoted to finding efficient search-heuristics.

The aim of this Special Issue is to present and highlight novel algorithms, architectures, techniques, and applications for image feature extraction and selection.

Dr. Pier Luigi Mazzeo
Guest Editor

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