1. ECE172A Project Report Image Search and Classification Isaac Caldwell

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).