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Computer Vision Metrics: Chapter Five (Part B)

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For Part A of Chapter Five, please click here.

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


General Vision Metrics Taxonomy

To understand feature metrics, we develop a Vision Metrics Taxonomy composed of summary criteria. Each criterion is selected with a practical, engineering perspective in mind to provide information for evaluation and implementation in specific terms, such as algorithm, spectra, memory size, and other attributes. The basic categories of the Vision Metrics Taxonomy are shown in Table 5-1, and also summarized here as a list, and each list item is discussed in separate sections in this chapter:

  • Feature Descriptor Family
  • Spectra Dimension
  • Spectra Value
  • Interest Point
  • Storage Format
  • Data Types
  • Descriptor Memory
  • Feature Shape
  • Feature Pattern
  • Feature Density
  • Feature Search Method
  • Pattern Pair Sampling
  • Pattern Region Size
  • Distance Function
  • Run-Time Compute


Table 5-1. Vision Metrics Taxonomy

Many of the background concepts used in the taxonomy are discussed in Chapter 4, where attributes about the internal structure and goals of common features are analyzed. In addition, this taxonomy is illustrated in the Feature Metric Evaluation (FME) information tables later in this chapter. A small subset of the taxonomy is used in the Chapter 6 survey of feature descriptors to record summary information. The taxonomy in Table 5-1 is a guideline for collecting and summarizing information. No judgment on goodness or performance is recorded or implied.

Feature Descriptor Family

As described at the beginning of this chapter, feature descriptors are classified in this taxonomy as follows:

  • Local Binary Descriptors
  • Spectra Descriptors
  • Basis Space Descriptors
  • Polygon Shape Descriptors

Spectra Dimensions

The spectra or values recorded in the feature descriptor vary, and may include one or more types of information or spectra. We divide the categories as follows:

  • Single variate: stores a single value such as...