Bookmark and Share

Computer Vision Metrics: Chapter Five

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:


Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


Taxonomy of Feature Description Attributes

“for the Entwives desired order, and plenty, and peace (by which they meant that things should remain where they had set them).”
—J. R. R. Tolkien, Lord of the Rings

This chapter develops a general Vision Metrics Taxonomy for feature description, so as to collect summary descriptor attributes for high-level analysis. The taxonomy includes a set of general robustness criteria for feature description and ground truth datasets. The material presented and discussed in this book follows and reflects this taxonomy. By developing a standard vocabulary in the taxonomy, terms and techniques are intended to be consistently communicated and better understood. The taxonomy is used in the survey of feature descriptor methods in Chapter 6 to record ‘what’ practitioners are doing.

As shown in Figure 5-1, the Vision Metrics Taxonomy is based on feature descriptor dimensions using three axes—shape and pattern, spectra, and density—intended to create a simple framework for analysis and discussion. A few new terms and concepts have been introduced where there had been no standard, such as for the term feature descriptor families. These have been broken down into categories of local binary descriptors, spectra descriptors, basis space descriptors, and polygon shape descriptors; these descriptor families are also discussed in detail in Chapter 4. Additionally, the taxonomy borrows some useful terminology from the literature when it exists there, including several terms for the robustness and invariance attributes.


Figure 5-1. Taxonomy for feature descriptor dimensions, including (1) feature density as global, regional, and sparse local; (2) shape and pattern of pixels used to compute the descriptor, which includes rectangles, circles, and sparse sampling patterns; (3) spectra, which includes the spectrum of information contained in the feature itself

Why create a taxonomy that is guaranteed to be fuzzy, includes several variables, and will not perfectly express the attributes of any feature descriptor? The intent is to provide a framework to describe various design approaches used for feature description. However, the taxonomy is not intended to be used for comparing descriptors in terms of their goodness, performance, or accuracy.

The three axes of the Vision Metrics Taxonomy are:

    ...