# Introduction to the Bag of Features Paradigm for Image Classification and Retrieval Source: [Introduction to the Bag of Features Paradigm for Image Classification and Retrieval]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:35:40.298Z Authors: Stephen O'Hara, Bruce A. Draper Published: 2011-01-17T23:27:08.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()