Automatic visual dictionary generation through Optimum-Path Forest clustering

• Autor(a) principal: Afonso, L. [UNESP]
• Data de Publicação: 2012
• Outros Autores: Papa, J. [UNESP], Papa, L. [UNESP], Marana, Aparecido Nilceu [UNESP], Rocha, Anderson
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ICIP.2012.6467255; http://hdl.handle.net/11449/73809
• topic: Automatic Visual Word Dictionary Calculation; Bag-of-visual Words; Clustering algorithms; Optimum-Path Forest; Discriminative features; Graph-based clustering; Image Categorization; Invariant points; Optimum-path forests; State-of-the-art techniques; User intervention; Vision communities; Visual dictionaries; Visual word; Forestry; Image processing; Algorithms; Image Analysis

Image 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.