Finding similar images quicky using object shapes

  • Authors:

  • Heng Tao Shen

  • Affiliations:

  • National University of Singapore

  • Venue:
  • Proceedings of the tenth international conference on Information and knowledge management
  • Year:
  • 2001

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Abstract

Retrieving images from a large image collection has been an active area of research. Most of the existing works have focused on content representation. In this paper, we address the issue of identifying relevant images quickly. This is important in order to meet the users' performance requirements. We propose a framework for fast image retrieval based on object shapes extracted from objects within images. The framework builds a hierarchy of approximations on object shapes such that shape representation at a higher level is a coarser representation of a shape at the lower level. In other words, multiple shapes at a lower level can be mapped into a single shape at a higher level. In this way, the hierarchy serves to partition the database at various granularities. Given a query shape, by searching only the relevant paths in the hierarchy, a large portion of the database can thus be pruned away. We propose the angle mapping (AM) method to transform a shape from one level to another (higher) level. AM essentially replaces some edges of a shape by a smaller number of edges based on the angles between the edges, thus reducing the complexity of the original shape. Based on the framework, we also propose two hierarchical structures to facilitate speedy retrieval. The first, called Hierarchical Partitioning on Shape Representation (HPSR), uses the shape representation as the indexing key. The second, called Hierarchical Partitioning on Angle Vector (HPAV), captures the angle information from the shape representation. We conducted an extensive study on both methods to see their quality and efficiency. Our experiments on sets of images, each of which has objects around from 1 to 30, showed that the framework can provide speedy image retrieval without sacrificing on the quality. Both proposed schemes can improve the efficiency by as much as hundreds of times to sequential scanning. The improvement grows as image database size, objects per image or object dimension increase.