Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification

We address the problem of visual category recognition by learning an image-to-image distance function that attempts to satisfy the following property: the distance between images from the same category should be less than the distance between images from different categories. We use patch-based feature vectors common in object recognition work as a basis for our image-to-image distance functions. Our large-margin formulation for learning the distance functions is similar to formulations used in the machine learning literature on distance metric learning, however we differ in that we learn local distance functions-a different parameterized function for every image of our training set--whereas typically a single global distance function is learned. This was a novel approach first introduced in Frome, Singer, & Malik, NIPS 2006. In that work we learned the local distance functions independently, and the outputs of these functions could not be compared at test time without the use of ...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik
• Keywords: Computer Vision, Distance Functions, Global Distance Function, ICCV 2007, Image-to-image Distance Functions, Local Distance Functions, Visual Category Recognition