Scalable Image Searching Method based on Orientation Code Density

This paper aims to propose a fast image searching method from environmental observation images even in the presence of scale changes. A new scheme has been proposed for extracting feature areas based on a robust image registration algorithm called Orientation code matching. Extracted areas are stored as template images and utilized in image searching. As the number of template images grows, the searching cost becomes a serious problem. Additionally, changes of viewing positions cause scale change of an image and matching failure. In our scheme, richness in features is important for feature area generation and the entropy is used to evaluate the variety of edge directions which are stable to scale change of the image. This characteristic contributes to limitation of searching area and reduction in calculation costs. Scaling factors are estimated by orientation code density which means the percentage of effective codes in fixed size areas. An estimated scaling factor is applied to matching a scale of template images to one of observation images. Some experiments are performed in order to compare computation time and verify effectiveness of estimated scaling factor using real scenes.