Afonso, Luis Cláudio Súgi [UNESP]Papa, João Paulo [UNESP]Papa, Luciene Patrici [UNESP]Marana, Aparecido Nilceu [UNESP]Rocha, Anderson2015-03-182015-03-182012-01-012012 Ieee International Conference On Image Processing (icip 2012). New York: Ieee, p. 1897-1900, 2012.1522-4880http://hdl.handle.net/11449/117646Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation.1897-1900engOptimum-Path ForestClustering algorithmsBag-of-visual WordsAutomatic Visual Word Dictionary CalculationTrabalho apresentado em eventoWOS:000319334901236Acesso aberto90391829327471946027713750942689