Combining Texture and Shape Descriptors for Bioimages Classification: A Case of Study in ImageCLEF Dataset

Autor:	Pedro Henrique Bugatti, Thiago Pereira Colonhezi, Fabrício Martins Lopes, Anderson Brilhador
Rok vydání:	2013
Předmět:	business.industry Computer science Volume (computing) Subject (documents) Feature selection Image processing Texture (music) Machine learning computer.software_genre Pipeline (software) Task (project management) Pattern recognition (psychology) Artificial intelligence Data mining business computer
Zdroj:	Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783642418211 CIARP (1)
DOI:	10.1007/978-3-642-41822-8_54
Popis:	Nowadays a huge volume of data e.g. images and videos are daily generated in several areas. The importance of this subject has led to a new paradigm known as eScience. In this scenario, the biological image domain emerges as an important research area given the great impact that it can leads in real solutions and people's lives. On the other hand, to cope with this massive data it is necessary to integrate into the same environment not only several techniques involving image processing, description and classification, but also feature selection methods. Hence, in the present paper we propose a new framework capable to join these techniques in a single and efficient pipeline, in order to characterize biological images. Experiments, performed with the ImageCLEF dataset, have shown that the proposed framework presented notable results, reaching up to 87.5% of accuracy regarding the plant species classification, which is highly relevant and a non-trivial task.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5593f1f2d859ab21574f7e3424a8c177 https://doi.org/10.1007/978-3-642-41822-8_54 Zobrazit plný text záznamu