Image indexing with mixture hierarchies
We present an image indexing method based on a hierarchical description of the density of each of the image classes in a given database. The method is similar in spirit to traditional agglomerative clustering procedures but produces a complete mixture density, instead of a representative point, at each node of the indexing tree. Estimation of the density at a given node only requires knowledge of the mixture parameters of the children nodes, not the original data. The process is very flexible and efficient, therefore suited to problems involving large databases where existing groupings may have to be combined, or new groupings created, frequently. Experimental results show that the new indexing structure consistently outperforms a linear search when both efficiency and retrieval accuracy are taken into account.