  The past decade has seen the growing popularity of Bag of Features (BoF)
approaches to many computer vision tasks, including image classification, video
search, robot localization, and texture recognition. Part of the appeal is
simplicity. BoF methods are based on orderless collections of quantized local
image descriptors; they discard spatial information and are therefore
conceptually and computationally simpler than many alternative methods. Despite
this, or perhaps because of this, BoF-based systems have set new performance
standards on popular image classification benchmarks and have achieved
scalability breakthroughs in image retrieval. This paper presents an
introduction to BoF image representations, describes critical design choices,
and surveys the BoF literature. Emphasis is placed on recent techniques that
mitigate quantization errors, improve feature detection, and speed up image
retrieval. At the same time, unresolved issues and fundamental challenges are
raised. Among the unresolved issues are determining the best techniques for
sampling images, describing local image features, and evaluating system
performance. Among the more fundamental challenges are how and whether BoF
methods can contribute to localizing objects in complex images, or to
associating high-level semantics with natural images. This survey should be
useful both for introducing new investigators to the field and for providing
existing researchers with a consolidated reference to related work.
