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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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Image search and classification isaac caldwell l.jpg

Image Search and Classification

Isaac Caldwell

ECE172A Project Report


Motivation l.jpg
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 l.jpg
Related Research

  • Perona/CalTech – Unsupervised.

  • Boutell/UofRochester – Trained with whole images.


Approach l.jpg
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.


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Cost Analysis

  • Indicate the financial advantages for the customer

  • Compare quality and price with those of the competition




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Results

  • Not so great..


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Results

  • Three Categories: Sky, Foliage, Dirt


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Results

  • Closeup of the last slide...


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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.


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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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