Object categorization from image search: Difference between revisions

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{{outdatedupdate|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

| booktitlebook-title = Proc. IEEE International Conference on Computer Vision

| url = http://vision.cs.princeton.edu/documents/FergusFei-FeiPeronaZisserman_ICCV05.pdf

| year = 2005}}

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=== Unrelated images ===

One problem with using Internet image search results as a training set for a classifier is the high percentage of unrelated images within the results. It has been estimated that, when a search engine such as Google images is queried with the name of an object category (such as ''airplane?''), up to 85% of the returned images are unrelated to the category.<ref name = "fergus"/>

=== Intra-class variability ===

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|last = Hofmann

|title = Probabilistic Latent Semantic Analysis

|booktitlebook-title = Uncertainty in Artificial Intelligence

|year = 1999

|url = httphttps://www.cs.brown.edu/~th/papers/Hofmann-UAI99.pdf

|url-status = dead

|archiveurlarchive-url = https://web.archive.org/web/20070710083034/http://www.cs.brown.edu/~th/papers/Hofmann-UAI99.pdf

|archivedatearchive-date = 2007-07-10

}}</ref>

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| first = Li-Jia |author2=Wang, Gang |author3=Fei-Fei, Li

| title = OPTIMOL: automatic Online Picture collection via Incremental MOdel Learning

| booktitlebook-title = Proc. IEEE Conference on Computer Vision and Pattern Recognition

| year = 2007

| url = http://vision.cs.princeton.edu/documents/LiWangFei-Fei_CVPR2007.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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| issue = 476

| page = 1566

| citeseerx = 10.1.1.5.9094 | s2cid = 7934949 }}

</ref>

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{{cite conference

| last = Fergus

| first = R. |author2=Perona, P. |author3=Zisserman, A.

| title = A visual category filter for Google images

| booktitlebook-title = Proc. 8th European Conf. on Computer Vision

| year = 2004

| url = http://www.robots.ox.ac.uk/~fergus/papers/Fergus_ECCV4.pdf

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|author2=Forsyth, D.

| title = Animals on the web

| booktitlebook-title = Proc. Computer Vision and Pattern Recognition

| year = 2006

| doi = 10.1109/CVPR.2006.57

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|author2=Barnard, K.

| title = Probabilistic web image gathering

| booktitlebook-title = ACM SIGMM workshop on Multimedia information retrieval

| year = 2005

| url = http://portal.acm.org/citation.cfm?id=1101838

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== References ==

<references/>

== External links ==

{{Empty section|date=July 2010}}

== See also ==