Leveraging Topological Information for Semantic Segmentation


Leveraging Topological Information for Semantic Segmentation – A major challenge in semantic segmentation is the difficulty in using multiple information sources for the same semantic object. Semantic segmentation is an essential step towards this goal. Many approaches have been used to solve semantic segmentation problems based on semantic pairwise similarity; therefore, these approaches also have a direct impact on semantic model learning for a semantic segmentation problem. In addition, several approaches have been proposed that can enhance the semantic segmentation results. In this paper, one such approach is based on semantic pairwise similarity. In this framework, two semantically similar semantic models is learned from multiple semantic pairwise similarity. The semantic model is composed of three parts: a semantic semantic model which detects semantic relationships between objects and a model that learns semantic similarity between semantic pairwise pair of objects. The semantic model can be used to learn semantic relationships between semantic objects. The semantic model learns semantic relationship between semantic pairs of semantic objects. Experimental results on real-world datasets show that the proposed approach works better than other semantic segmentation and semantic joint learning approaches.

This paper addresses the problem of learning the shape space of a high-dimensional (H) dimensional data set. To that end our contribution is a Bayesian framework that learns the shape space of the data set by solving a general Bayesian optimization problem. The framework shows that the H-dimensional data sets are not very compact, and hence a novel optimization problem is approached. The proposed framework is evaluated using the PASCAL VOC dataset, where it outperforms state-of-the-art methods in terms of accuracy and complexity.

We demonstrate how to recover an image with low-level features by solving low-level semantic image restoration problems. We propose a new approach that uses visual attention, by exploiting the fact that the human gaze can only appear in the visual domain. The technique is simple: we first extract low-level features from the image. Then the human gaze is recovered by means of our method. The proposed approach is evaluated on different synthetic datasets that provide very promising results.

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Leveraging Topological Information for Semantic Segmentation

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  • An Improved Clustering Method with Improved Variational Inference

    Unsupervised feature learning using adaptive thresholding for object clusteringThis paper addresses the problem of learning the shape space of a high-dimensional (H) dimensional data set. To that end our contribution is a Bayesian framework that learns the shape space of the data set by solving a general Bayesian optimization problem. The framework shows that the H-dimensional data sets are not very compact, and hence a novel optimization problem is approached. The proposed framework is evaluated using the PASCAL VOC dataset, where it outperforms state-of-the-art methods in terms of accuracy and complexity.

    We demonstrate how to recover an image with low-level features by solving low-level semantic image restoration problems. We propose a new approach that uses visual attention, by exploiting the fact that the human gaze can only appear in the visual domain. The technique is simple: we first extract low-level features from the image. Then the human gaze is recovered by means of our method. The proposed approach is evaluated on different synthetic datasets that provide very promising results.


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