Afonso, L. [UNESP]Papa, J. [UNESP]Papa, L. [UNESP]Marana, Aparecido Nilceu [UNESP]Rocha, Anderson2014-05-272014-05-272012-12-01Proceedings - International Conference on Image Processing, ICIP, p. 1897-1900.1522-4880http://hdl.handle.net/11449/73809Image 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. © 2012 IEEE.1897-1900engAutomatic Visual Word Dictionary CalculationBag-of-visual WordsClustering algorithmsOptimum-Path ForestDiscriminative featuresGraph-based clusteringImage CategorizationInvariant pointsOptimum-path forestsState-of-the-art techniquesUser interventionVision communitiesVisual dictionariesVisual wordForestryImage processingAlgorithmsImage AnalysisTrabalho apresentado em evento10.1109/ICIP.2012.6467255Acesso aberto2-s2.0-848758181636027713750942689