Automatic visual dictionary generation through optimum-path forest clusteringautomatic visual dictionary generation through optimum-path forest clustering

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.

Subjective assessment of adhesives usage by complete denture wearers in a brazilian population

The information about the knowledge and usage of denture adhesives is still limited, particularly in Brazilian populations. Objective: The purpose of this study was to investigate the aspects related to the use or nonuse of denture adhesives by complete denture wearers. Material and method: Questionnaires were applied to 100 bimaxillary complete denture wearers concerning their usage of denture adhesives. The survey was carried out according to three categories: those who had never tried adhesive, those who had tried adhesive at least once but no longer used it, and those who currently used it. Result: Of the 100 participants, 80% had never tried adhesives, 16% had tried at least once but no longer used it and 4% had used on a regular basis. Conclusion: Within the limitations of this study, the results of this subjective assessment suggest that within the participants that had never tried adhesives, 77.5% inform that the adhesive is not needed, and all participants who regularly use it inform its efficiency.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A comparison about evolutionary algorithms for optimum-path forest clustering optimization

In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.