Learning object segmentation from video data

Abstract

This memo describes the initial results of a project to create aself-supervised algorithm for learning object segmentation from videodata. Developmental psychology and computational experience havedemonstrated that the motion segmentation of objects is a simpler,more primitive process than the detection of object boundaries bystatic image cues. Therefore, motion information provides a plausiblesupervision signal for learning the static boundary detection task andfor evaluating performance on a test set. A video camera andpreviously developed background subtraction algorithms canautomatically produce a large database of motion-segmented images forminimal cost. The purpose of this work is to use the information insuch a database to learn how to detect the object boundaries in novelimages using static information, such as color, texture, and shape.This work was funded in part by the Office of Naval Research contract#N00014-00-1-0298, in part by the Singapore-MIT Alliance agreement of11/6/98, and in part by a National Science Foundation Graduate StudentFellowship.