Representation of object similarity in human vision: psychophysics and a computational model. Cutzu, Edelman. Vision Research 1997.

  1. Visual system is robust to illumination and perspective changes.  We usually hold that we should be sensitive to changes in shape, but how do you study that in a well principled way?
  2. References to earlier work that studied 2d shape change, here considering 3d
  3. 3 main ideas about how to make pose-independent shape classification, and there are ways to test which one seems to be what we do
  4. <Mostly interested in the way they generate their shape data and properties of it, so skipping most of the other stuff>
    1.  ex/ “theories such as Shepard’s law of generalization, Nosofsky’s GCM and Ashby’s GRT”
  5. Shapes made up bodies – in all they were 70-dimensional
  6. Screen Shot 2015-07-15 at 4.58.54 PM
  7. “We remark that the nonlinearities in the image creation process led to a complicated relationship between the shape-space representation of an object and its appear- ance on the screen”

  8. “Many early studies relied on the estimation of subjective similarities between stimuli, through a process in which the ob- server had to provide a numerical rating of similarity when presented with a pair of stimuli. One drawback of this method is that many subjects do not feel comfort-

    able when forced to rate similarity on a numerical scale. Another problem is the possibility of subjects modifying their internal similarity scale as the experiment pro- gresses. We avoided these problems by employing two different methods for measuring subjective similarity: compare pairs of pairs (CPP) and delayed match to sample (DMTS).”

  9. <skipping different experimental designs, moving on to discussion>
  10. Running MDS on subject data puts points pretty much where they should be
  11. “The CPP experiments described above support the hypothesis of veridical representation of similarity, by demonstrating that it is possible to recover the true low-dimensional shape-space configuration of complex stimuli from proximity tables obtained from subjects who made forced-choice similarity judgments.”
  12. “It is important to realize that the major computa- tional accomplishment in the experiments we have de- scribed so far is that of the human visual system and not of the MDS procedure used to analyze the data.”
  13. “The detailed recovery from subject data of complex similarity patterns imposed on the stimuli supports the notion of veridical representation of similarity, dis- cussed in the introduction. Although our findings are not inconsistent with a two-stage scheme in which geometric reconstruction of individual stimuli precedes the computation of their mutual similarities, the com- putational model that accompanies these findings offers a more parsimonious account of the psychophysical data. Specifically, representing objects by their similari- ties to a number of reference shapes (as in the RBF model described in Section 6.2) allowed us to replicate the recovery of parameter-space patterns observed in human subjects, while removing the need for a prior reconstruction of the geometry of the objects.”
  14. “Assuming that perceptual simi- larities decrease monotonically with psychological space distances, multidimensional scaling algorithms derive the psychological space configuration of the stimulus points from the table of the observed similarities.”
  15. asd

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