Extracting the Recurring Patterns from Image

This paper presents a preliminary research on extracting the recurring patterns from an image. In practice, the recurring patterns have high correlation with the objects that appear recurrently in the image. We assume that the patterns only undergo shift transformation in the image. The main idea of our method is to use the local context information for detecting the 2D distinguishable sub -patterns (DSP) that provide us the spatial information of the patterns in the image with high confidence level. The 2D DSPs are distinctive features of the patterns. The computation is then focused on the signals around those DSPs and eventually grows the DSPs into patterns. To handle the noisy image, we develop dynamic local K -means to quantize the image. The shapes and the position of the DSPs are found in the quantized image. The pattern growing procedure is performed on the original noisy image based on the information provided by the DSPs.