Learning object boundary detection from motion data
Author(s)
Ross, Michael G.
Kaelbling, Leslie P.
Date Issued
January 2004
Series/Report no.
Computer Science (CS);
Abstract
This paper describes the initial results of a project to create a self-supervised algorithm for learning object segmentation from video data. Developmental psychology and computational experience have demonstrated that the motion segmentation of objects is a simpler, more primitive process than the detection of object boundaries by static image cues. Therefore, motion information provides a plausible supervision signal for learning the static boundary detection task and for evaluating performance on a test set. A video camera and previously developed background subtraction algorithms can automatically produce a large database of motion-segmented images for minimal cost. The purpose of this work is to use the information in such a database to learn how to detect the object boundaries in novel images using static information, such as color, texture, and shape.
Subjects
machine learning
self-supervised algorithm
motion segmentation
object boundary detection
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