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Type: Journal article
Title: Deep learning on sparse manifolds for faster object segmentation
Author: Nascimento, J.
Carneiro, G.
Citation: IEEE Transactions on Image Processing, 2017; 26(10):4978-4990
Publisher: IEEE
Issue Date: 2017
ISSN: 1057-7149
Statement of
Jacinto C. Nascimento, Gustavo Carneiro
Abstract: We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning-based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i.e., a training that does not require a vast amount of annotated images to estimate robust appearance and shape models) and a fast search process during inference. Therefore, it is desirable that the dimensionality of this rigid transformation space is as small as possible in order to enhance the advantages brought by the aforementioned division of the problem. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection space. Furthermore, we propose the use of deep belief networks to allow for a training process that can produce robust appearance models without the need of large annotated training sets. We test our approach in the segmentation of the left ventricle of the heart from ultrasound images and lips from frontal face images. Our experiments show that the use of sparse manifolds and deep belief networks for the rigid detection stage leads to segmentation results that are as accurate as the current state of the art, but with lower search complexity and training processes that require a small amount of annotated training data.
Keywords: Deep belief networks; deformable objects, non-rigid segmentation, sparse manifold
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
RMID: 0030074807
DOI: 10.1109/TIP.2017.2725582
Grant ID:
Appears in Collections:Computer Science publications

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