[互联网]山东大学 基于Web的信息检索和知识发现chap17图像检索3.ppt

Shape-based Image Retrieval 　 4 Image Retrieval by Shape Similarity The Split-and-Merge Algorithm 由两个阶段组成 1）由上而下的，通过对图像进行划分来发现同质的区域 2）由下而上地，通过对邻近同质的区域的合并满足第2个性质。 The Split-and-Merge Algorithm First, we perform splitting: At the start of the algorithm, the entire image is considered as the candidate region. If the candidate region does not meet the homogeneity criterion, we split it into four smaller candidate regions. This is repeated until there are no candidate regions to be split any more. Then, we perform merging: Check all pairs of neighboring regions and merge them if it does not violate the homogeneity criterion. The Split-and-Merge Algorithm Sample image The Split-and-Merge Algorithm First split The Split-and-Merge Algorithm Second split The Split-and-Merge Algorithm Third split The Split-and-Merge Algorithm Merge The Split-and-Merge Algorithm Final result Region Finding There are a variety of algorithms for region finding. Another example is “Smooth Region Analysis” (ATT Cambridge): Design a filter that detects smoothness of color and  texture, apply the filter to create a smoothness image, find closed regions of high smoothness. Region Finding Sample image Region Finding Smoothness image Region Finding Resulting regions shaded to their mean color Chain Codes（Contour description) An interesting way of describing a contour is using chain codes. A chain code is an ordered list of local orientations (directions) of a contour. These local directions are given through the locations of neighboring pixels, so there are only eight different possibilities. We assign each of these directions a code: Chain Codes Then we start at the first edge in the list and go clockwise around the contour. We add the code for each edge to a list, which becomes our chain code. Chain Codes What happens if in our chain code for a given contour we replace every code n with (n mod 8) + 1 ? The contour will be rotated clockwise by 45 degrees. We can also compute the derivat