image search and classification isaac caldwell l.
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ECE172A Project Report

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Image Search and Classification Isaac Caldwell. ECE172A Project Report. Motivation. Develop image processing algorithms that allow searching directly on the image, not in the image tags. The basic concept is a 2D Google search. . Related Research. Perona/CalTech – Unsupervised.

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Presentation Transcript
motivation
Motivation
  • Develop image processing algorithms that allow searching directly on the image, not in the image tags.
  • The basic concept is a 2D Google search.
related research
Related Research
  • Perona/CalTech – Unsupervised.
  • Boutell/UofRochester – Trained with whole images.
approach
Approach
  • Unsupervised approach relies on heavier processing. Not going anywhere in 4 weeks.
  • Training
    • Features: complexity and color.
    • K-means separation fails as sample space overlaps. No distinct clusters.
    • Nearest Neighbor requires delineating training sets.
cost analysis
Cost Analysis
  • Indicate the financial advantages for the customer
  • Compare quality and price with those of the competition
results
Results
  • Not so great..
results9
Results
  • Three Categories: Sky, Foliage, Dirt
results10
Results
  • Closeup of the last slide...
improvements
Improvements
  • Expand the training data and improve its quality.
  • Adding detected sector properties ( beyond {E,R,G,B}.)
  • Kill the nasty bug in the entropy scaling.
closing
Closing
  • Replace the core engine.
    • The concept of an “image-in, image-out” search engine really needs to be unsupervised.
    • The implementation has potential as a segmentation scheme.
    • Some work on the mapping output could be used as an image classifier (lots of sky or lots of dirt).
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