A New Shape Descriptor and Segmentation Algorithm for Automated Classifying of Multiple-morphological Filamentous Algae

In our previous work on automated microalgae classification system we proposed the multi-resolution image segmentation that can handle well with unclear boundary of algae bodies and noisy background, since an image segmentation is the most important preprocessing step in object classification and recognition. The previously proposed approach was able to classify twelve genera of microalgae successfully; however, when we extended it to work with new genera of filamentous algae, new challenging problems were encountered. These difficulties arise due to a variety of the forms of filamentous algae, which complicates both image segmentation and classification processes, resulting in substantial degradation of classification accuracy. Thus, in this work we propose a modified version of our multi-resolution segmentation algorithm by combining them in such a way that the strengths of both algorithms complement each other’s weaknesses. We also propose a new skeleton-based shape descriptor to alleviate an ambiguity caused by multiple morphologies of filamentous forms of algae in classification process. Effectiveness of the two proposed approaches are evaluated on five genera of filamentous microalgae. SMO is used as a classifier. Experimental result of 91.30% classification accuracy demonstrates a significant improvement of our proposed approaches.