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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.

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