Interactive noise-controlled boundary image matching using the time-series moving average transform

In this paper we propose a time-series matching-based approach that provides the interactive boundary image matching with noise control for a large-scale image database. To achieve the noise reduction effect in boundary image matching, we exploit the moving average transform of time-series matching. We are motivated by a simple intuition that the moving average transform might reduce the noise of boundary images as well as that of time-series data. To confirm this intuition, we first propose a new notion of k-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our k-order image matching identifies similar boundary images in this time-series domain by comparing the k-moving average transformed sequences. We then propose an index-based method that efficiently performs k-order image matching on a large image database, and formally prove its correctness. We also formally analyze the relationship of orders and their matching results and present an interactive approach of controlling the noise reduction effect. Experimental results show that our k-order image matching exploits the noise reduction effect well, and our index-based method outperforms the sequential scan by one or two orders of magnitude. These results indicate that our k-order image matching and its index-based solution provide a very practical way of realizing the noise control boundary image matching. To our best knowledge, the proposed interactive approach for large-scale image databases is the first attempt to solve the noise control problem in the time-series domain rather than the image domain by exploiting the efficient time-series matching techniques. Thus, our approach can be widely used in removing other types of distortions in image matching areas.