【paper】Elastic Matching of User Sketches

via this paper

Retrieval by shape similarity, given a user-sketched template is particularly challenging, owing to the difficulty to derive a similarity measure that closely conforms to the common perception of similarity by humans.

  • Matching by shape is complicated by the fact that a shape does not have a mathematical definition that exactly matches what the user feels as a shape. 
  • Well-known distance measures commonly used in mathematics are not suitable to represent shape similarity as perceived by humans
  • Human perception is not a mere interpretation of a retinal patch, but an active interaction between the retinal patch
    and a representation of our knowledge about objects.

related work

  • QVE system: evaluating the correlation between a linear sketch and edge images in the database (High values of correlation require user-drawn shape close to the shape database)
  • shapes are represented as

    an ordered set of boundary features.

    Each boundary is coded as an ordered sequence of vertices of its polygonal approximation.

    • Features are collections of a fixed number of vertices.

    • roughly evaluate

      similarity as the distance between the boundary feature 

      vector of the query and those associated with the target


    • Boundary features of objects in database images are

      organized into a quite complex index tree structure.

  • QBIC system:
    • Shape representation based on global features such as area, circularity, eccentricity, major axis orientation and moment invariants
    • shape similarity is evaluated as the weighted Euclidean 

      distance in a low dimensional feature space.

there is no warranty that our notion of perceptive closeness is mapped into the topological closeness in the feature space.

Elastic matching promises to approximate human ways of perceiving similarity and to possess a remarkable robustness to shape distortion.

  • the sketch is deformed to adjust itself to the shapes of the objects in the images.
  • The match between the deformed sketch and

    the imaged object, as well as the elastic deformation energy 

    spent in the warping are used to evaluate the similarity

    between the sketch and the image.

  • The elastic matching is

    integrated with arrangements to provide scale and partial 

    rotation invariance, and with filtering mechanisms to prune

    the database.


an image I, its luminance at every point normalized in [0,1], we search for a contour with a shape similar to that of sketched template.

in general, the image will contain no contour exactly equal to the template.

It is not just a matter of noisy images, which we can, to a limited extent, model and cope with. The image and the template
can be different to begin with. This makes traditional template matching brittle.

To make a robust match even in the presence of deformations, we must allow the template to wrap. This takes into account two opposite requirements:

  1. it must follow as closely as possible the edges of the image Ie.
  2. the deformation of the template – elastic deformation energy for template, which depends only on the first and second derivatives of the deformation.
  3. In order to discover the similarity between the original shape of the template and the shape of the edge areas on the image, we must set some constraints on deformation.


Elastic matching is one of the pattern recognition techniques in computer science.

Elastic matching (EM) is also known as deformable templateflexible matching, or nonlinear template matching.

Elastic matching can be defined as an optimization problem of two-dimensional warping specifying corresponding pixels between subjected images.

Application(signature recognition):

  • Two categories of methods and tools for the analysis of dynamic signatures are presented.
  1. The first, measure analysis, is introduced and used to show how imitations can be differentiated unambiguously from genuine examples.
  2. The second category of elastic matching of signatures is believed to follow the type of mechanism which our visual cortex might use when we examine a pair of signatures.
    • Imagine the reference signature traced onto a transparent elastic sheet. If this is then placed over the reference signature and stretched, they superimpose. A feature specifying the degree of similarity is then the elastic energy contained within the elastic sheet.
    • A complementary feature measures the degree to which the two signatures overlap after the stretching process, the local correlation. The elastic matching method works from these two features.
The linkages between points on two authentic signatures found by elastic matching.
The linkages between points on two authentic signatures found by elastic matching.
The extracted circular arcs from two static authentic signatures and their linkages chosen by elastic matching.
The extracted circular arcs from two static authentic signatures and their linkages chosen by elastic matching.
  • Face Recognition using Elastic Bunch Graph Matching (EGBM) fisherface


object recognition by elastic graph matching (dynamic link matching): L. Wiskott, J.-M. Fellous, N. Krueger and von der Malsburg C.(1997) Face recognition by elastic bunch graph matching.IEEE PAMI 19: 775–779.

Eckes C, Triesch J, von der Malsburg C. (2006) Analysis of cluttered scenes using an elastic matching approach for stereo images. Neural Comput. 18(6):1441-71.


Face Recognition: An Introduction


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