Hierarchical Data Structures for Pattern Recognition
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Pattern recognition is an important area with potential applications in computer vision, Speech understanding, knowledge engineering, bio-medical data classification, earth sciences, life sciences, economics, psychology, linguistics, etc. Clustering is an unsupervised classification process corning under the area of pattern recognition. There are two types of clustering approaches: 1) Non-hierarchical methods 2) Hierarchical methods. Non-hierarchical algorithms are iterative in nature and. perform well in the context of isotropic clusters. Time-complexity of these algorithms is order of (0 (n) ) and above, Hierarchical agglomerative algorithms, on the other hand, are effective when clusters are non-isotropic. The single linkage method of hierarchical category produces a dendrogram which corresponds to the minimal spanning tree, conventional approaches are time consuming requiring O (n2 ) computational time. In this thesis we propose an intelligent partitioning scheme for generating the minimal spanning tree in the co-ordinate space. This is computationally elegant as it avoids the computation of similarity between many pairs of samples me minimal spanning tree generated can be used to produce C disjoint clusters by breaking the (C-1) longest edges in the tree. A systolic architecture has been proposed to increase the speed of the algorithm further. Simulation study has been conducted and the corresponding results are reported. The simulation package has been developed on DEC-1090 in Pascal. It is observed based on the simulation study that the parallel implementation reduces the time enormously. The number of processors required for the parallel implementation is a constant making the approach more attractive. Texture analysis and synthesis has been extensively studied in the context of computer vision, Two important approaches which have been studied extensively by researchers earlier are statistical and structural approaches, Texture is understood to be a periodic pattern with primitive sub patterns repeating in a particular fashion. This has been used to characterize texture with the help of the hierarchical data structure, tree. It is convenient to use a tree data structure as, along with the operations like merging, splitting, deleting a node, adding a node, etc, .it would be useful to handle a periodic pattern. Various functions like angular second moment, correlation etc, which are used to characterize texture have been translated into the new language of hierarchical data structure.