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|Type:||Artigo de evento|
|Title:||Image Categorization Through Optimum Path Forest And Visual Words|
|Abstract:||Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.|
|Citation:||Proceedings - International Conference On Image Processing, Icip. , v. , n. , p. 3525 - 3528, 2011.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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