Talk:Bag-of-words model in computer vision

Context and clarity
This article seems to need some more context. Is this a widely-pursued avenue of research in computer vision? Or is it the product of a single research group? Many parts of the article are unclear. Just to pick a couple:
 * It is very widely pursued. It is the most common type of descriptor for entire images. Bag-of-Words models are sometimes used for more local representations, too (e.g. regions, bounding boxes) Jhhays (talk) 08:45, 10 January 2014 (UTC)
 * In the introduction, it says "Here an image may refer to a particular object, such as an image of a car". Now, I'd think that "object" refers to the car, and that the image of the car is in fact the image. Which is exactly what you'd expect an image to be. Why the confusing statement about what "image" refers to?
 * The "dictionary" in the representation section seems to be, in fact, an ordered list. The "dictionary" property of it (being able to set and access arbitrary keys, not just a sequence of consecutive integers) is not used at all. It's also unclear what this has to do with images, and it sounds like it's simply describing a particular clunky implementation of the NLP bag of words model instead of saying anything informative about it. (A less clunky dictionary would represent a histogram by mapping words to counts, as in
 * Also, the counts as they appear in that example are incorrect.
 * The article refers to several images that were removed because they had no license or source information. The author of these sections either needs to release the appropriate images under a free license, or rewrite the article to not depend on images.

Finally, the article needs to be more up-front about the fact that the features it is detecting are not, in fact, "words", especially given how it starts by giving examples that involve words.  r speer  / ɹəəds ɹ  21:52, 9 August 2008 (UTC)

It's definitely a widespread computer vision technique. I'm not sure I'd call it an active field of research, however, since it's pretty well understood (and surpassed) at this point.
 * It's definitely an active research area. There is a steady stream of new work on feature quantization, compact features, how to include spatial information, etc. Jhhays (talk) 08:45, 10 January 2014 (UTC)

I'm hoping to fix up this article, once I have a bit of spare time. Ha ha, right. Mdockrey (talk) 00:46, 26 November 2008 (UTC)

This is still an active field of study, as in people still publish on this topic. Its not particularly nascent and as a matter of opinion not particularly fruitful lately. I edited for grammar because "Also the BoW model for object segmentation and localization is also lack of study," is not a sentence. Zoratao (talk) 18:54, 7 May 2013 (UTC)

I'm deleting the word "sparse" from the introduction. Bag of Words models are sometimes sparse. For instance, when doing large scale retrieval with an inverted index the Bag-of-words need to be sparse to make the index compact. But for many recognition tasks the histograms of visual words are not sparse at all. When using certain more advanced feature encoding schemes the histograms are very dense. Jhhays (talk) 08:45, 10 January 2014 (UTC)

This page fails to cite the single most relevant paper on the topic: Video Google from Sivic and Zisserman, 2003. http://www.robots.ox.ac.uk/~vgg/research/vgoogle/ The paper has nearly 3000 citations. Jhhays (talk) 08:48, 10 January 2014 (UTC)

Missing figure(s)
One section refers to a figure, but no figure is displayed within the document. — Preceding unsigned comment added by 67.186.198.46 (talk) 02:26, 9 September 2011 (UTC)

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