Pollen recognition using a multi-layer hierarchical classifier

We propose a method to recognize pollen grains using a two-stage classifier. First, texture classification categorizes the pollen grains into sub-groups. Then, a final classification of individual pollen types is done by segmenting the image int multiple layers of regions for each pollen image. The main novelty in our method is threefold: (1) Adopting two successive classification stages. (2) Combining hierarchical clustering and SVM algorithms to merge similar pollen types into sub-groups. (3) Adopting a layering approach prior to performing feature extraction. The combination of these aspects gives excellent results. We evaluated our method using 1,063 light-microscopy images of pollen grains from 30 species. The results show that: (1) the layering technique increases the classification rate by almost almost 7% over using the same features directly. (2) adopting two classification stage increases the classification rate by 6%. (3) the proposed system outperformed traditional techniques.

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