A Comparative Study of Clustering Validation Indices and Maximum Entropy for Sintonization of Automatic Segmentation Techniques

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:30:27Z
Fecha de publicación2019-01-01
ResumenAutomatic image segmentation is a fundamental task in many applications such as video surveillance, image retrieval, medical image analysis, recognition, tracking and objects classification. This task is not easy due to the complexity of image features as well as the number of objects within the images which is unknown in most of the time. However, the correct tuning of the parameters associated with the automatic segmentation algorithms can improve the levels of precision in the object segmentation in the images. This paper proposes the use of clustering validation indices and maximum entropy as cost functions to quantify the quality of segmentation in order to find the optimal parameters of the automatic segmentation techniques. The impact of using clustering validation indices and maximum entropy was evaluated in the automatic segmentation algorithms, K-Means, Watershed and Statistical Region Merging (SRM), using four image databases with images containing different numbers and sizes of objects and different levels of illumination. The results obtained reveal that the use of clustering validation indices and maximum entropy for tuning the automatic segmentation algorithms results in better segmentations with a number of segmented objects close to what really exists in the images, while these results are competitive with the works reported in the literature.es_ES
Doihttps://doi.org/10.1109/tla.2019.8932330es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/2423
Idiomaeses_ES
EditorialInstitute of Electrical and Electronics Engineers (IEEE)es_ES
RelaciónIEEE Latin America Transactionses_ES
URL relacionadohttps://doi.org/10.1109/tla.2019.8932330es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteIEEE Latin America Transactions
TítuloA Comparative Study of Clustering Validation Indices and Maximum Entropy for Sintonization of Automatic Segmentation Techniqueses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorRESÉNDIZ, JACIEL DAVID HERNÁNDEZ
AutorCASTRO, HEIDY MARISOL MARIN
AutorLEAL, EDGAR TELLO
AutorRESÉNDIZ, JACIEL DAVID HERNÁNDEZes_ES
AutorCASTRO, HEIDY MARISOL MARINes_ES
AutorLEAL, EDGAR TELLOes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número08es_ES
Rango de páginas1229-1236es_ES
URL relacionadahttps://doi.org/10.1109/tla.2019.8932330
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen17es_ES

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