Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks

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Publication Type pre-print
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Creator Seyedhosseini Tarzjani, Seyed Mojtaba
Other Author Sajjadi, Mojtaba; Tasdizen, Tolga
Title Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks
Date 2013-01-01
Description Contextual information plays an important role in solving vision problems such as image segmentation. However, extracting contextual information and using it in an effective way remains a difficult problem. To address this challenge, we propose a multi-resolution contextual framework, called cascaded hierarchical model (CHM), which learns contextual information in a hierarchical framework for image segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. We repeat this procedure by cascading the hierarchical framework to improve the segmentation accuracy. Multiple classifiers are learned in the CHM; therefore, a fast and accurate classifier is required to make the training tractable. The classifier also needs to be robust against overfitting due to the large number of parameters learned during training. We introduce a novel classification scheme, called logistic disjunctive normal networks (LDNN), which consists of one adaptive layer of feature detectors implemented by logistic sigmoid functions followed by two fixed layers of logical units that compute conjunctions and disjunctions, respectively. We demonstrate that LDNN outperforms state-of-theart classifiers and can be used in the CHM to improve object segmentation performance.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 2168
Last Page 2175
Language eng
Bibliographic Citation Seyedhosseini, M., Sajjadi, M., & Tasdizen, T. (2013). Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks. Proceedings of the IEEE International Conference on Computer Vision, 6751380, 2168-75.
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Date Created 2014-05-12
Date Modified 2021-05-06
ID 712538
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