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Application of Support Vector Machine to Heterotrophic Bacteria Colony Recognition

In allusion to the present heterotrophic bacteria colony counting method having the disadvantages of subjectivity, big error and low efficiency, we proposed a recognition method for heterotrophic bacteria colony based on SVM. After a series of pre-processing and segmentation to the acquired colony image, 6 feature parameters such as: area, perimeter, equivalent diameters of colony individual and non-colony ones were extracted. Then we adopted SVM to recognize them and the recognition rate of 98.7% was obtained. This means the effectiveness of the feature extraction method and the feasibility of support vector machines used for heterotrophic bacteria colony recognition.