R*-Tree Based Similarity and Clustering Analysis for Images

R*-Tree Based Similarity and Clustering Analysis for Images

Jiaxiong Pi (University of Nebraska at Omaha, USA), Yong Shi (University of Nebraska at Omaha, USA) and Zhengxin Chen (University of Nebraska at Omaha, USA)
DOI: 10.4018/978-1-61692-859-9.ch003
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Image content analysis plays an important role for adaptive multimedia retrieval. In this chapter, the authors present their work on using a useful spatial data structure, R*-tree, for similarity analysis and cluster analysis of image contents. First, they describe an R*-tree based similarity analysis tool for similarity retrieval of images. They then move on to discuss R*-tree based clustering methods for images, which has been a tricky issue: although objects stored in the same R* tree leaf node enjoys spatial proximity, it is well-known that R* trees cannot be used directly for cluster analysis. Nevertheless, R* tree’s indexing feature can be used to assist existing cluster analysis methods, thus enhancing their performance of cluster quality. In this chapter, the authors report their progress of using R* trees to improve well-known K-means and hierarchical clustering methods. Based on R*-Tree’s feature of indexing Minimum Bounding Box (MBB) according to spatial proximity, the authors extend R*-Tree’s application to cluster analysis containing image data. Two improved algorithms, KMeans-R and Hierarchy-R, are proposed. Experiments have shown that KMeans-R and Hierarchy-R have achieved better clustering quality.
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Basics Of R* Trees

Just like a B-Tree, an R-Tree (Guttman 1984) relies on a balanced hierarchical structure, in which each tree node is mapped to a disk page. However, whereas B-Trees are built on single-value keys and rely on a total order on these keys, R-Trees organize rectangles according to a containment relationship. Each object to be indexed will be represented by Minimum Bounding Box (MBB) in the index structure except point for which an MBB simply degrades to a point. All indexed objects will eventually be put in leaf nodes. A leaf node contains an array of leaf entries. A leaf entry is a pair (mbb, oid), where mbb is the Minimum Bounding Box (MBB) and oid is the object ID. Each internal node is associated with a rectangle, referred to as the directory rectangle (dr), which is the minimal bounding box of the rectangle of its child nodes. The structure of R-Tree satisfies the following properties:

  • 1 For all nodes in the tree (except for the root), the number of entries is between m and M, where 0≤mM/2.

  • 2 For each entry (dr, node-id) in a non-leaf node N, dr is the directory rectangle of a child node of N, whose page address is node-id.

  • 3 For each leaf entry (mbb, oid), mbb is the minimal bounding box of spatial component of the object stored at address oid.

  • 4 The root has at least two entries (unless it is a leaf).

  • 5 All leaves are at the same level.

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