Using a neural net to instantiate a deformable model
Authors: Michael Revow, Geoffrey E. Hinton Christopher K. I. Williams
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Deformable models are  an attractive approach to recognizing  non(cid:173) rigid objects which have considerable within class variability.  How(cid:173) ever,  there  are  severe  search  problems  associated  with  fitting  the  models to data.  We show that by using neural networks to provide  better starting points, the search time can be significantly reduced.  The method is  demonstrated on a  character  recognition task. 
In previous work  we  have  developed  an approach to handwritten character recogni(cid:173) tion  based  on  the use  of deformable models  (Hinton,  Williams and  Revow,  1992a;  Revow,  Williams and Hinton,  1993).  We have obtained good performance with this  method, but a major problem is that the search  procedure for fitting each model to  an  image is  very  computationally intensive,  because  there  is  no efficient  algorithm  (like  dynamic programming) for  this  task.  In this paper  we  demonstrate that it is  possible  to  "compile  down"  some of the  knowledge  gained  while  fitting  models  to  data to obtain better  starting points that significantly reduce  the search  time. 
1  DEFORMABLE MODELS FOR DIGIT RECOGNITION 
The basic idea in using deformable models for digit recognition is that each digit has  a  model, and  a  test  image is  classified  by finding  the model which  is  most likely to  have generated it.  The quality of the match between model and test image depends  on the  deformation of the model, the amount of ink that is  attributed to noise  and  the  distance of the  remaining ink from  the  deformed model. 
·Current  address:  Department of Computer  Science  and  Applied  Mathematics,  Aston 
University,  Birmingham  B4  7ET,  UK. 
966 
Christopher K.  T.  Williams,  Michael  D.  Revow,  Geoffrey  E.  Hinton 
More  formally,  the two  important terms in  assessing  the fit  are  the prior  probabil(cid:173) ity  distribution  for  the  instantiation  parameters of a  model  (which  penalizes  very  distorted models),  and the imaging model that characterizes  the probability distri(cid:173) bution over  possible images given  the instantiated  model l .  Let  I  be an  image,  M  be a  model  and  z  be  its instantiation parameters.  Then the evidence  for  model M  is  given  by 
P(IIM) = J P(zIM)P(IIM, z)dz 
(1) 
The first  term in the integrand is the prior on the instantiation parameters and the  second  is  the  imaging model  i.e.,  the  likelihood of the  data given  the  instantiated  model.  P(MII) is  directly  proportional to P(IIM), as  we  assume  a  uniform prior  on each  digit.  Equation 1 is  formally correct,  but if z has more than a few  dimensions the evalua(cid:173) tion of this integral is very  computationally intensive.  However,  it is often  possible  to make an  approximation based on  the  assumption  that the integrand is strongly  peaked around  a  (global)  maximum value  z*.  In this case,  the evidence  can  be  ap(cid:173) proximated by the highest  peak of the integrand times a  volume factor  ~(zII, M),  which  measures  the sharpness of the  peak2 . 
P(IIM) ~ P(zIM)P(Ilz, M)~(zII, M) 
(2)  By  Taylor expanding  around  z*  to second  order  it  can  be  shown  that the  volume  factor  depends  on  the  determinant of the  Hessian  of 10gP(z, 11M)  .  Taking logs  of equation  2,  defining  EdeJ  as  the  negative  log  of P(z*IM),  and  EJit  as  the  cor(cid:173) responding  term  for  the  imaging model,  then  the  aim of the  search  is  to  find  the  minimum of E tot  =  EdeJ  + EJit .  Of course  the  total energy  will  have  many local  minima;  for  the  character  recognition  task  we  aim  to find  the  global  minimum by  using  a  continuation method (see  section  1.2). 
1.1  SPLINES, AFFINE TRANSFORMS  AND  IMAGING  MODELS 
This section  presents  a  brief overview  of our  work  on  using  deformable models for  digit recognition.  For a fuller  treatment, see  Revow,  Williams and  Hinton (1993) . 
Each  digit is  modelled by  a  cubic  B-spline  whose  shape  is  determined  by  the  posi(cid:173) tions of the control points in the object-based frame.  The models have eight control  points,  except  for  the  one  model which  has  three,  and  the  seven  model which  has  five.  To  generate  an  ideal  example of a  digit  the  control  points  are  positioned  at  their  "home"  locations.  Deformed  characters  are  produced  by  perturbing  the  con(cid:173) trol  points  away  from  their  home  locations.  The  home  locations  and  covariance  matrix for  each  model were  adapted in order to improve the  performance. 
The deformation energy only penalizes shape  deformations.  Affine  transformations,  i.e., translation, rotation,  dilation, elongation, and shear,  do  not change the under(cid:173) lying shape of an  object so  we  want the  deformation energy  to be  invariant under  them .  We  achieve  this  by  giving  each  model  its  own  "object-based  frame"  and  computing the deformation energy  relative to this frame. 
lThis framework  has been  used  by many  authors,  e.g.  Grenander  et  al (1991) .  2The  Gaussian  approximation  has  been  popularized  in  the  neural  net  community  by