From Local Features to Global Shape Constraints: Heterogeneous Matching Scheme for Recognizing Objects Under Serious Background Clutter
Language
English
Type
Proceedings of International Conferences
Authors
Martin Klinkigt, Koichi Kise
Title
From Local Features to Global Shape Constraints: Heterogeneous Matching Scheme for Recognizing Objects Under Serious Background Clutter
Journal
Asian Conference on Computer Vision
Vol.
4
Pages
pp.2150-2161
Publisher
Springer
Reviewed
Reviewed
Date
November 2010
Key
Klinkigt2010_ACV (id=1179)
Abstract
Object recognition in computer vision is the task to categorize images based on their content. With the absence of background clutter in images high recognition performance can be achieved.
In this paper we show how the recognition performance is improved even with a high impact of background clutter and without additional information about the image.
For this task we segment the image into patches and learn a geometric structure of the object. In evaluations we first show that our system is of comparable performance to other state-of-the-art system and that for a difficult dataset the recognition performance is improved by 13.31%.
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