Paper Info
Title
Image Segmentation Using A Generic, Fast And Non-Parametric Approach
Abstract
In this paper, we investigate image segmentation by region merging. Given any similarity measure between regions, satisfying some weak constraints, we give a general predicate for answering if two regions are to be merged or not during the segmentation process. Our predicate is generic and has six properties. The first one is its independance with respect to the similarity measure, that leads to a user-independant and adaptative predicate. Second, it is non-parametric, and does not rely on any assumption concerning the image. Third, due to its weak constraints, knowledge may be included in the predicate to fit better to the user's behaviour. Fourth, provided the similarity is well-chosen by the user, we are able to upperbound one type of error made during the image segmentation. Fifth, it does not rely on a particular segmentation algorithm and can be used with almost all region-merging algorithms in various application domains. Sixth, it is calculated quickly, and can lead with appropriated algorithms to very efficient segmentation.
Year
DOI
Venue
1998
10.1109/TAI.1998.744885
TENTH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS
Keywords
Field
DocType
vision and image processing, AI algorithms, machine learning
Scale-space segmentation,Similarity measure,Pattern recognition,Image texture,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Region growing,Artificial intelligence,Minimum spanning tree-based segmentation,Machine learning
Conference
ISSN
Citations 
PageRank 
1082-3409
1
0.35
References 
Authors
9
2
Name
Order
Citations
PageRank
Christophe Fiorio119723.27
Richard Nock210.35