04-06-2012, 11:58 AM
IMAGE MATCHING
IMAGE MATCHING.pdf (Size: 4.05 MB / Downloads: 99)
Motivation for Image Recognition
•Image panoramas
•Image watermarking
•Global robot localization
•Face Detection
•Optical Character Recognition
•Manufacturing Quality Control
•Content-Based Image Indexing
•Object Counting and Monitoring
•Automated vehicle parking systems
•Visual Positioning and tracking
•Video Stabilization
What does object recognition involve?
•Identification
•Detection
•Object categorization
•Scene and context categorization
•Tracking
•Action Recognition
•Events
Clustering
•Iterative.
•Agglomerative clustering–Add token to cluster if token is similar enough to element of clusters
•–Repeat
•Divisive clustering–Split cluster into subclustersif tokens are dissimilar enough within cluster
•–Boundary separates subclustersbased on similarity
•–Repeat
K-Means Clustering
•Initialization: Given K categories, N points in feature space. Pick K points randomly; these are initial cluster centers (means) m1, …,mK. Repeat the following:
•
•1. Assign each of the Npoints, xj , to clusters by nearest mi
•2. Re-compute mean miof each cluster from its member points
•3. If no mean has changed more than some ε, stop
The Overall System
•Device sends location to server, the server returns the image features of the current kernel to the phone, where they are organized into a search structure (KD-tree, ANN = fast Approximate Nearest Neighbor search).
•Camera takes an image, extracts features, compares to features in the cache, runs RANSAC to find out whether individually matched features are consistent with other matches, and finally displays the information of the top ranked image.
•The database on the server is built by collecting a set of geotagged and POI (point-of-interest) tagged images, which are then grouped together based on their location and allocated to loxels, features are extracted, and then we try to match images to other images in the loxel.
•A similar RANSAC consistency check is run, as during run-time on the phone, cluster features that tend to easily match features in other images, and mark features that cannot be matched again as low-priority features that are sent to handset last, if at all. The features are compressed and stored in the database.