Disadvantage of Shape Context

Using the shape context in cluttered scenes is unreliable.

The shape context descriptor alone is not powerful enough to yield reliable point correspondences in cluttered scenes.

  • It is difficult to recover the scale parameter, since normalizing the radial distances by the man/median point distances will no longer work.
  • Object and non-object points close to the object are hard to distinguish on the basis of their shape context alone.
  • Points which are close to each other on the model shape are often matched to points which are far away from each other in the image.
  • Shape contexts are not invariant under arbitrary affine transforms, but the log-polar binning ensures that, for small locally affine distortion due to pose change, intra-category variation etc., the change in the shape context is correspondingly small.
  • The richness of the shape context makes it robust to noise and occlusion, but it is too complex and time-consuming.

solutions:

  1. RANSAC: if some of the correspondences are correct, to identify outliers in the alignment phase of the iteration process using a robust estimation scheme. Outliers can then be excluded from the next shape context computation.
  2. Shapemes: vector quantization on the shape context.
    • Vector quantization involves clustering the vectors and then representing each vector by the index of the cluster that it belongs to.
    • k-means clustering to obtain k shapemes: all of shape context from known set are considered as points in a d-dimensional space, so each know view is a collection of shapemes, each d bin shape context is quantized to its nearest shapeme and replaced by the shapeme label{1,…,k}.
    • A known view is simplified into a histogram of shapeme frequencies. From s d-bin histogram to a single k-bin histogram

Using Shapemes for Mid-level Vision

How do we model context?

  • One way is to find a computational/operational definition for each mid-level visual cue, such as continuity, convexity, or parallelism.
  • An alternative is to use a generic “context” descriptor, such as shape context or geometric blur.
  1. Using edge orientation:
    • Only matching points with similar gradient orientation.
  2. Shape Context with Figural Continuity
    • The neighboring points on the model shape U should map to points on the target shape V which are also close to each other.
    • The correspondences are the sum of shape context costs and the continuity cost.
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