Finding Clusters within a Class to Improve Classification Accuracy

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Finding Clusters within a Class to Improve
Classification Accuracy

• Literature Survey
• Yong Jae Lee
• 3/6/08

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Object Recognition

• Determine which, if any, of a given set of
  objects appear in a given image or video

UT tower
Trees
Statue
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Problem Statement

• A problem of matching models of objects built
  from a database with object models found in novel
  images
• Representation of object model is important
• Need to learn a model from train set

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Object Cars

• Example Car images returned from Google

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Find Clusters
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Object Representation key paper 1

• Scale Invariant Feature Transform (SIFT) Lowe.
  2004
• Local features based on the appearance of the
  object at particular interest points
• Thresholded image gradients are sampled over
  16x16 array of locations
• Create array of orientation histograms
• 8 orientations x 4x4 histogram array 128
  dimensions

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Compute Similarity key paper 2

• Proximity Distribution Kernels Ling et al. 2007
• Address the spatial relation between local
  features
• Invariant to scale, rotation, translation

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Clustering key paper 3

• Normalized Cuts Shi et al. 2001
• Graph theoretic approach to clustering
• Measure the goodness of partition by formulating
  the objective as an eigenvalue problem
• Maximize the within cluster similarity relative
  to the across cluster difference
• of clusters must be given

X1 X2 X3 X4
X1 K11 K12 K13 K14
X2 K21 K22 K23 K24
X3 K31 K32 K33 K34
X4 K41 K42 K43 K44
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Classification key paper 4

• Support Vector Machines Vapnik et al. 1995
• Discriminative Classifier based on optimal
  separating hyperplane
• Margin of separation the separation between the
  hyperplane and the closest data point

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• Infinite possible hyperplanes

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• SVM Learning finds the a hyperplane for which the
  margin of separation is maximized

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Questions
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References

• H. Ling and S. Soatto, Proximity Distribution
  Kernels for Geometric Context in Category
  Recognition, IEEE 11th International Conference
  on Computer Vision, pp. 1-8, 2007.
• D. Lowe, Distinctive Image Features from
  Scale-Invariant Keypoints," International Journal
  of Computer Vision, vol. 60, no. 2, pp. 91-110,
  2004.
• J. Shi and J. Malik, Normalized cuts and image
  segmentation," IEEE Transactions on Pattern
  Analysis and Machine Intelligence, vol. 22, no.
  8, pp. 888-905, 2000.
• C. Cortes and V. Vapnik, Support-vector
  networks," Machine Learning, vol. 20, no. 3, pp.
  273-297, 1995.

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PDK