Simultaneous Object Recognition and Segmentation by Image Exploration

Vittorio Ferrari, Tinne Tuytelaars, Luc Van Gool

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Methods based on local, viewpoint invariant features have proven capable of recognizing objects in spite of viewpoint changes, occlusion and clutter. However, these approaches fail when these factors are too strong, due to the limited repeatability and discriminative power of the features. As additional shortcomings, the objects need to be rigid and only their approximate location is found. We present an object recognition approach which overcomes these limitations. An initial set of feature correspondences is first generated. The method anchors on it and then gradually explores the surrounding area, trying to construct more and more matching features, increasingly farther from the initial ones. The resulting process covers the object with matches, and simultaneously separates the correct matches from the wrong ones. Hence, recognition and segmentation are achieved at the same time. Only very few correct initial matches suffice for reliable recognition. Experimental results on still images and television news broadcasts demonstrate the stronger power of the presented method in dealing with extensive clutter, dominant occlusion, large scale and viewpoint changes. Moreover non-rigid deformations are explicitly taken into account, and the approximative contours of the object are produced. The approach can extend any viewpoint invariant feature extractor.
Original languageEnglish
Title of host publicationToward Category-Level Object Recognition
EditorsJean Ponce, Martial Hebert, Cordelia Schmid, Andrew Zisserman
PublisherSpringer Berlin Heidelberg
Pages145-169
Number of pages25
ISBN (Electronic)978-3-540-68795-5
ISBN (Print)978-3-540-68794-8
DOIs
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume4170
ISSN (Print)0302-9743

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