GAMM: genetic algorithms with meta-models for vision

Recent adaptive image interpretation systems can reach optimal performance for a given domain via machine learning, without human intervention. The policies are learned over an extensive generic image processing operator library. One of the principal weaknesses of the method lies with the large size of such libraries, which can make the machine learning process intractable. We demonstrate how evolutionary algorithms can be used to reduce the size of the operator library, thereby speeding up learning of the policy while still keeping human experts out of the development loop. Experiments in a challenging domain of forestry image interpretation exhibited a 95% reduction in the average time required to interpret an image, while maintaining the image interpretation accuracy of the full library. Categories and Subject Descriptors I.4.8 [Computing Methodologies]: Image Processing and Computer Vision—Scene Analysis General Terms Performance Keywords genetic algorithms, machine learning, he...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Greg Lee, Vadim Bulitko
• Keywords: Adaptive Image Interpretation, GECCO 2005, Image Interpretation, Machine Learning