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Invariant-based shape retrieval in pictorial databases

  • Michael Kliot
  • Ehud Rivlin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)

Abstract

One of the strongest cues for retrieval of content information from images is shape. However, due to the wide range of transformations that an object might undergo, this is also the most difficult one to handle. It seems that shape retrieval is one of the major barriers nowadays on the way of image databases to become commonly used. Common approaches use global attributes (Faloutsos et al. [1]), feature points (Pentland et al. [2]), histograms (Jain and Vailaya [3]), or physical models of deformations (Del Bimbo and Pala [4]). We present an approach for shape retrieval from pictorial databases which is based on invariant features of the image. In particular we use a combination of semi-local multi-valued invariant signatures and global features. Spatial relations and global properties are used to eliminate non-relevant images before similarity is computed. Common approaches usually don't handle viewpoint transformations more complex than similarity and require the full shape in order to compute image features. The advantages of the proposed approach are its ability to handle images distorted by different viewpoint transformations, its ability to retrieve images even in situations in which part of the shape is missing (i.e., in case of occlusion or sketch-based queries), and its ability to support efficient indexing.

We have implemented our approach in a heterogeneous database having a SQL-like user interface augmented with sketch-based queries. The system is built on top of a commercial database system, and can be activated from the Web. We present experimental results demonstrating the effectiveness of the proposed approach.

Keywords

Input Image Image Retrieval Affine Transformation Road Sign Shape Retrieval 
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 1998

Authors and Affiliations

  • Michael Kliot
    • 1
  • Ehud Rivlin
    • 1
  1. 1.Computer Science DepartmentTechnion - Israel Institute of TechnologyHaifaIsrael

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