Image Database
Retrieval
NTT: Visit
1/7/99
Motivation from MIT ... | ||
Discuss current and related work | ||
Flexible Templates | ||
Complex Features | ||
Demonstrations | ||
Related NTT Efforts | ||
Discussion of collaboration | ||
Future work | ||
Dinner |
Image Databases are proliferating | |||
The Web | |||
Commercial Image Databases | |||
Video Databases | |||
Catalog Databases | |||
“Find me a bag that looks like a Gucci.” | |||
Virtual Museums | |||
“Find me impressionist portraits.” | |||
Travel Information | |||
“Find me towns with Gothic architecture.” | |||
Real-estate | |||
“Find me a home that is sunny and open.” |
There is a very wide variety of images...
Search for images containing waterfalls?
Finding the right features | |||
Insensitive to movement of components | |||
Sensitive to critical properties | |||
Focussing attention | |||
Not everything matters | |||
Generalization based on class | |||
Given two images | |||
Small black dog & Large white dog | |||
(Don’t have much in common…) | |||
Return other dogs |
Motivation from MIT ... | ||
Discuss current and related work | ||
Flexible Templates | ||
Complex Features | ||
Demonstrations | ||
Related NTT Efforts | ||
Discussion of collaboration | ||
Future work | ||
Dinner |
Complex Feature Representation
Motivated by the Human brain… | ||
Infero-temporal cortex computes many thousand selective features | ||
Features are selective yet insensitive to unimportant variations | ||
Every object/image has some but not all of these features | ||
Retrieval involves matching the most salient features |
Resolution is reduced at each step…
Not every feature is useful for a query
Normalization of Signature Space
Incorporating Negative Examples
Diagonal approx. of Fisher’s Linear Discr. | |||
Weight features highly if: | |||
Variance of pos and neg is greater than | |||
Variance of pos alone | |||
Interesting Pattern Recognition Properties
The statistics of the data is non-gaussian | ||
Data is 45,000 dim. but highly redundant. | ||
PCA can be used to reduce dimension | ||
But, retrieval performance deteriorates (??) | ||
** Non-gaussian data! | ||
Retain only those features which are kurtotic | ||
45,000 down to 5,000 | ||
** Performance improves! | ||
Kurtotic features are those which are unusual | ||
Distinct, interesting. | ||
Kurtotic features require fewer bits |