Tuesday, September 9, 2014

Phase III: Labeling / Plan I

This article is of an experimental design for a part of my research plan (on language acquisition), where phonetic labels and visual patterns are to be associated with machine learning. 

  • Basic Ideas
    • Goal: The system relates figure images with linguistic labels (phoneme strings).
    • Labels represent the shapes or colors of figures.
    • Labels and shapes/colors of figures shall be learned in a non-supervised manner by presenting labels and figures together.
    • The system should be able to associate labels with shapes/colors of figures after learning.
  • Tools
    • Computer Vision: OpenCV/SimpleCV
    • Machine Learning Tool: TBD
      • Clustering (non-supervised learning)
        Hopefully, non-parametric for the number of clusters.
      • Association
        Auto-complete-like function for learned patterns. 
  • Steps
    1. Learning patterns
      • Input patterns (in two modalities)
        • Figure images: rectangles, circles, triangles of various sizes and colors
        • Label pattern: 2D array of (phoneme position × phoneme type)
      • Image processing: monochromatizing, edge detection, SIFT?
      • Learning
        Co-occurrence patterns of figures and labels, figure shapes, colors, and labels shall be clustered with the following configuration of learners:
        [the following has been modified on 2014-09-14]
        • Label learner: label candidates are selected from frequent phoneme N-grams.
        • Shape learner: assign a non-supervised learner for figure shapes.
        • Color learner: assign a non-supervised learner for figure colors.
          Colors are learned with color label candidates as teacher signals.
        • Cross-modal (label sense) learner: for a label (candidate) to have a sense, it must have a statistic correlation with a 'cluster' in shape/color learner.
    2. Association
      Hopefully, the non-supervised leaners used in 1 have associative, 
      auto-complete-like function for learned patterns.  Otherwise, some external mechanism should be added to associate labels (phoneme patterns) with shapes/colors and vice versa.
      In any case, as learned category nodes do not have complete information to construct particular lower input patterns, some external mechanism should be added to visualize the association.

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