Hierarchical data structures for picture storage, retrieval and classification

  • Y. T. Chien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 80)


This chapter discusses several approaches to tackle the problem of efficiently storing and accessing a large set of pictorial data for analysis and classification. The main idea behind these approaches deals with the use of hierarchical data structures that allow different levels of picture details to be stored and analyzed. By using this hierarchy, searching and classification of pictorial data can be much faster compared with conventional, sequential storage.

Two major hierarchical structures and their applications are presented. The first is applicable to situations where pictorial data have been transformed into multidimensional vectors. A hierarchical projection tree, which combines hierarchical clustering algorithms with one dimensional projection, provides an efficient means for storing and accessing these vectors. This data structure is especially useful for nearest neighbor search or classification. The second data structure discussed in this paper attempts to construct a hierarchy of pictorial data by utilizing their global characteristics, eliminating the need to extract feature vectors. A preliminary implementation and evaluation of this approach, based upon gray level histograms as a similarity measure, is presented.


Test Point Minimal Span Tree Near Neighbor Gray Level Histogram Multidimensional Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1980

Authors and Affiliations

  • Y. T. Chien
    • 1
  1. 1.Dept. of EECSUniv. of ConnecticutStorrs

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