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The past decade has seen the growing popularity of Bag of Features (BoF)approaches to many computer vision tasks, including image classification, videosearch, robot localization, and texture recognition. Part of the appeal issimplicity. BoF methods are based on orderless collections of quantized localimage descriptors; they discard spatial information and are thereforeconceptually and computationally simpler than many alternative methods. Despitethis, or perhaps because of this, BoF-based systems have set new performancestandards on popular image classification benchmarks and have achievedscalability breakthroughs in image retrieval. This paper presents anintroduction to BoF image representations, describes critical design choices,and surveys the BoF literature. Emphasis is placed on recent techniques thatmitigate quantization errors, improve feature detection, and speed up imageretrieval. At the same time, unresolved issues and fundamental challenges areraised. Among the unresolved issues are determining the best techniques forsampling images, describing local image features, and evaluating systemperformance. Among the more fundamental challenges are how and whether BoFmethods can contribute to localizing objects in complex images, or toassociating high-level semantics with natural images. This survey should beuseful both for introducing new investigators to the field and for providingexisting researchers with a consolidated reference to related work.

Introduction to the Bag of Features Paradigm for Image Classification and Retrieval