Object categorization from image search: Difference between revisions

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{{update|date=September 2019}}
In [[computer vision]], the problem of '''object categorization from image search''' is the problem of training a [[Statistical classification|classifier]] to recognize categories of objects, using only the[[image search]], i.e., images retrieved automatically with an Internet [[search engine]]. Ideally, automatic image collection would allow classifiers to be trained with nothing but the category names as input. This problem is closely related to that of [[content-based image retrieval]] (CBIR), where the goal is to return better image search results rather than training a classifier for image recognition.
 
Traditionally, classifiers are trained using sets of images that are labeled by hand. Collecting such a set of images is often a very time-consuming and laborious process. The use of Internet search engines to automate the process of acquiring large sets of labeled images has been described as a potential way of greatly facilitating computer vision research.<ref name = "fergus">
{{cite conference
| last = Fergus
| first = R. |author2=Fei-Fei, L. |author3=Perona, P. |author4=Zisserman, A.
| title = Learning Object Categories from Google抯 Image Search
| book-title = Proc. IEEE International Conference on Computer Vision
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|book-title = Uncertainty in Artificial Intelligence
|year = 1999
|url = httphttps://www.cs.brown.edu/~th/papers/Hofmann-UAI99.pdf
|url-status = dead
|archive-url = https://web.archive.org/web/20070710083034/http://www.cs.brown.edu/~th/papers/Hofmann-UAI99.pdf
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{{cite journal
| last = Teh
| first = Yw |author2=Jordan, MI |author3=Beal, MJ |author4=Blei, David
| title = Hierarchical Dirichlet Processes
| journal = Journal of the American Statistical Association
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{{cite conference
| last = Fergus
| first = R. |author2=Perona, P. |author3=Zisserman, A.
| title = A visual category filter for Google images
| book-title = Proc. 8th European Conf. on Computer Vision