Abstract
In the last two chapters, we discussed the principles of some classic feature coding algorithms and two types of taxonomy for feature coding. In this chapter, we will describe their mathematical representations and motivations according to Taxonomy II depicted in Sect. 2.2 and Fig. 2.3. Since this chapter is somewhat mathematically heavy, the readers (beginners in particular) are advised to pay more attention to the underlying motivations of various algorithms before delving into the details of complex equations. Only knowing the motivation is sufficient to understand the following chapters.
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Notes
- 1.
The derivative to \(\omega \), according to [7], makes little contribution to the performance. Thus, it is removed in IFK.
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Huang, Y., Tan, T. (2014). Representative Feature Coding Algorithms. In: Feature Coding for Image Representation and Recognition. SpringerBriefs in Computer Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45000-0_3
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