A Bayesian network-based tunable image segmentation algorithm for object recognition | IEEE Conference Publication | IEEE Xplore

A Bayesian network-based tunable image segmentation algorithm for object recognition


Abstract:

We present a Bayesian network-based tunable image segmentation algorithm that can be used to segment a particular object of interest (OOI). In tasks such as object recogn...Show More

Abstract:

We present a Bayesian network-based tunable image segmentation algorithm that can be used to segment a particular object of interest (OOI). In tasks such as object recognition, semantically accurate segmentation of the OOI is a critical step. Due to the OOI consisting of different-looking fragments, traditional image segmentation algorithms that are based on the identification of homogeneous regions tend to oversegment. The algorithm presented in this paper uses Multiple Instance Learning to learn prototypical representations of each fragment of the OOI and a Bayesian network to learn the spatial relationships that exist among those fragments. The Bayesian network, as a probabilistic graphical model, in turn becomes evidence that is used for the process of semantically accurate segmentation of future instances of the OOI. The key contribution of this paper is the inclusion of domain-specific information in terms of spatial relationships as an input to a conventional Bayesian network structure learning algorithm. Preliminary results indicate that the proposed method improves segmentation performance.
Date of Conference: 14-17 December 2011
Date Added to IEEE Xplore: 13 February 2012
ISBN Information:
Print ISSN: 2162-7843
Conference Location: Bilbao, Spain

References

References is not available for this document.