Local and Global Features Extracting and Fusion for Microbial Recognition

It is presented that extraction of global and local feature based on digital microbial image analysis and fusion local and global features for microbe recognition. The global features are extracted by invariant moments and co-occurrence matrix, in which invariant moments computation is simplified by computing geometric moment and central moment based on the edge of microbe instead of the field of it. Curvature changing detection for microbe is characterized as local features by wavelet transform. Min-max was applied for normalization and after the fusion of normalized global match degree and normalized local match degree, the recognition result is the class that included the template image corresponding to the largest fused match degree. The experimental results show that fusing local and global features is effective for microbe image analysis and recognition.