A Classification System for Gray Scale Images by Using Combination of Simple Figures

It is thought that human being recognizes a complicated figure by combining simple figures. This is "figure alphabet hypothesis" and these simple figures are called "figure alphabet". We considered "the mechanism in which a complicated figure is recognized with the combination of the figure chosen from comparatively simple figure groups", and applies it to a pattern classification. The proposed method assumes the figure alphabet to be the dot pattern (Alphabet Dot Pattern, ADP) of an N × N pixels. Because there are many kinds of ADP, ADP group is optimized by Genetic Algorithm (GA). And, the euclidean distance of an input figure and an ADP group is calculated, and classifies the figure. The proposal technique was previously applied to the classification problem of the binary multifont figure, and the validity was shown. In this research, the result applied to the gradation images. As a result, the classification of the face image and the pedestrian image obtained a high correct answer rate.