Images as Bags of Pixels

We propose modeling images and related visual objects as bags of pixels or sets of vectors. For instance, gray scale images are modeled as a collection or bag of (X, Y, I) pixel vectors. This representation implies a permutational invariance over the bag of pixels which is naturally handled by endowing each image with a permutation matrix. Each matrix permits the image to span a manifold of multiple configurations, capturing the vector set's invariance to orderings or permutation transformations. Permutation configurations are optimized while jointly modeling many images via maximum likelihood. The solution is a uniquely solvable convex program which computes correspondence simultaneously for all images (as opposed to traditional pairwise correspondence solutions). Maximum likelihood performs a nonlinear dimensionality reduction, choosing permutations that compact the permuted image vectors into a volumetrically minimal subspace. This is highly suitable for principal components a...

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Tony Jebara
• Keywords: Computer Vision, ICCV 2003, Permutation Configurations, Permutation Transformations, Permuted Image Vectors, Pixels Representation, Pixels Subspace Benefits