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IMAGE SEGMENTATION FOR PEOPLE IDENTIFICATION: ANEVALUATION OF UNSUPERVISED TECHNIQUES

SEGMENTAÇÃO DE IMAGENS PARA IDENTIFICAÇÃO DE PESSOAS: UMA AVALIAÇÃO DE TÉCNICAS NÃO SUPERVISIONADAS

Santos, Lucas Lisboa dos; Pagano, Tiago ; Vacaro, Juliano ; Loureiro, Rafael ; Junior, Neilton ; Cunha, Guilherme da ; Nascimento, Erick Giovani Sperandio ; Winkler, Ingrid ;

Artigo completo:

The evaluation of segmentation techniques is a complex activity since itdepends on the target purpose. Our research is a technical evaluation ofsegmentation, specifically, it aims to evaluate the techniques Ant Colony FuzzyC-means Hybrid Algorithm (AFHA), Region Splitting and Merging Fuzzy C-meansHybrid Algorithm (RFHA) with the distance between points and Kanezaki, to identifypeople in images from the perspective of Jaccard Index and F Measure metrics(J&F). The method was divided into four stages: the selection of the image sample,evaluation process, experiment execution, and results composed by segmentedimage, group, and J&F metrics. The results indicate Kanezaki has surpassed theother techniques. It is recommended future research to identify whether a correlationbetween quantitative and qualitative analysis exists.

Artigo completo:

A avaliação das técnicas de segmentação é uma atividade complexa, pois depende do objetivo da segmentação. Nossa pesquisa é uma avaliação de técnicas de segmentação, mais especificamente ela tem como objetivo avaliar as técnicas Ant Colony Fuzzy C-means Hybrid Algorithm (AFHA), Region Splitting and Merging Fuzzy C-means Hybrid Algorithm (RFHA) com variações na distância entre pontos e Kanezaki, para identificar pessoas em imagens sob perspectiva das métrica métricas Jaccard Index e F Measure (J&F). O método foi dividido em quatro etapas: seleção da amostra de imagens, processo de avaliação, execução do experimento e a obtenção dos resultados compostos por imagem segmentada, grupo e a métrica J&F. Os resultados indicam que a técnica Kanezaki superou as demais. Pesquisas futuras são recomendadas para identificar se existe correlação entre as análises quantitativa e qualitativa.

Palavras-chave: : Image Segmentation; Machine Learning; Deep Learning; Segmentation Evaluation; Clustering,

Palavras-chave: Segmentação de Imagens; Aprendizado de Máquinas;Aprendizado Profundo; Avaliação da segmentação; Agrupamento,

DOI: 10.5151/siintec2020-IMAGESEGMENTATION

Referências bibliográficas
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Como citar:

Santos, Lucas Lisboa dos; Pagano, Tiago ; Vacaro, Juliano ; Loureiro, Rafael ; Junior, Neilton ; Cunha, Guilherme da ; Nascimento, Erick Giovani Sperandio ; Winkler, Ingrid ; "IMAGE SEGMENTATION FOR PEOPLE IDENTIFICATION: ANEVALUATION OF UNSUPERVISED TECHNIQUES", p. 635-643 . In: Anais do VI Simpósio Internacional de Inovação e Tecnologia. São Paulo: Blucher, 2020.
ISSN 2357-7592, ISBN: 2357-7592
DOI 10.5151/siintec2020-IMAGESEGMENTATION

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