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ECE172A Project Report

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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ECE172A Project Report

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  1. Image Search and Classification Isaac Caldwell ECE172A Project Report

  2. 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.

  3. Related Research • Perona/CalTech – Unsupervised. • Boutell/UofRochester – Trained with whole images.

  4. 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.

  5. Cost Analysis • Indicate the financial advantages for the customer • Compare quality and price with those of the competition

  6. High-level Representation...

  7. Processing

  8. Results • Not so great..

  9. Results • Three Categories: Sky, Foliage, Dirt

  10. Results • Closeup of the last slide...

  11. 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.

  12. 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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