Image segmentation by fusion of low level and domain specific information via Markov Random Fields

Karadag, Ozge Oztimur
Yarman Vural, Fatoş Tunay
We propose a new segmentation method by fusing a set of top-down and bottom-up segmentation maps under the Markov Random Fields (MRF) framework. The bottom-up segmentation maps are obtained by varying the parameters of an unsupervised segmentation method, such as Mean Shift. The top-down segmentation maps are constructed from some priori information, called domain specific information (DSI), received from a domain expert in the form of general properties about the image dataset. The properties are then used to generate domain specific maps (DSM) using logical predicates. Information gathered from the fusion of the bottom-up segmentation maps together with the domain specific maps are utilized to determine the pairwise potentials in the energy function of an unsupervised MRF model. Due to the inclusion of domain specific information, this approach can be considered as a first step to semantic image segmentation under an unsupervised MRF model. The proposed system is compared with the state of the art unsupervised image segmentation methods and satisfactory results are observed.


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Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2013-09-13)
A Markov Random Field based image segmentation system which combines top-down and bottom-up segmentation approaches is proposed in this study. The system is especially proposed for applications where no labeled training set is available, but some priori general information referred as domain specific information about the dataset is available. Domain specific information is received from a domain expert and formalized by a mathematical representation. The type of information and its representation depends o...
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The lack of boundary information in region based image segmentation algorithms resulted in many hybrid methods that integrate the complementary information sources of region and boundary, in order to increase the segmentation performance. In compliance with this trend, we propose a novel method to unify the region and boundary characteristics within the canonical Recursive Shortest Spanning Tree algorithm. The main idea is to incorporate the boundary information in the distance metric of RSST with minor cha...
Sahin, Kerem; Ulusoy, İlkay (2013-07-26)
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Similarity matrix framework for data from union of subspaces
Aldroubi, Akram; Sekmen, Ali; Koku, Ahmet Buğra; Cakmak, Ahmet Faruk (2018-09-01)
This paper presents a framework for finding similarity matrices for the segmentation of data W = [w(1)...w(N)] subset of R-D drawn from a union U = boolean OR(M)(i=1) S-i, of independent subspaces {S-i}(i=1)(M), of dimensions {d(i)}(i=1)(M). It is shown that any factorization of W = BP, where columns of B form a basis for data W and they also come from U, can be used to produce a similarity matrix Xi w. In other words, Xi w(i, j) not equal 0, when the columns w(i) and w(j) of W come from the same subspace, ...
Citation Formats
O. O. Karadag and F. T. Yarman Vural, “Image segmentation by fusion of low level and domain specific information via Markov Random Fields,” PATTERN RECOGNITION LETTERS, pp. 75–82, 2014, Accessed: 00, 2020. [Online]. Available: