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Pyramidal Model for Image Semantic Segmentation
2010 20th International Conference on Pattern Recognition, 2010We present a new hierarchical model applied to the problem of image semantic segmentation, that is, the association to each pixel in an image with a category label (e.g. tree, cow, building, ...). This problem is usually addressed with a combination of an appearance-based pixel classification and a pixel context model.
Giuseppe Passino +2 more
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Collaborative Semantic Segmentation with Image Labels
Proceedings of the 3rd International Conference on Video and Image Processing, 2019Weakly-supervised semantic segmentation has recently received much attention since it needs less fine-grained annotations than fully-supervised learning. Most existing studies use attention maps from the classification network as supervision, which suffers from only locating small discriminative parts of objects and lacking precise boundaries.
Zhikang Li +2 more
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Effective image restoration for semantic segmentation
Neurocomputing, 2020Abstract Recent semantic segmentation algorithms are greatly accelerated by deep convolutional neural networks (DCNNs). Although most of them perform well on normal images, they are not robust to the degenerations of images. To boost the performance of semantic segmentation on degraded images, we present an effective image restoration framework based
Xuejing Niu +3 more
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Image Segmentation by semantic method
Pattern Recognition, 1987Abstract The problem of region detection is addressed. Linear and quadratic approximation schemes are used to approximate the regions in an image. A set of attributes, which represent the properties of a region, are defined. A distance function, which has structural as well as semantic part, is introduced.
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On the use of regions for semantic image segmentation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, 2012There is a general trend in recent methods to use image regions (i.e. super-pixels) obtained in an unsupervised way to enhance the semantic image segmentation task. This paper proposes a detailed study on the role and the benefit of using these regions, at different steps of the segmentation process.
Rui Hu 0007 +2 more
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Semantic Image Segmentation and Object Labeling
IEEE Transactions on Circuits and Systems for Video Technology, 2007In this paper, we present a framework for simultaneous image segmentation and object labeling leading to automatic image annotation. Focusing on semantic analysis of images, it contributes to knowledge-assisted multimedia analysis and bridging the gap between semantics and low level visual features.
Thanos Athanasiadis +3 more
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Semantic image segmentation with deep features
2018 26th Signal Processing and Communications Applications Conference (SIU), 2018Deep convolutional neural networks (CNN) have shown significant success in many classification problems including semantic image segmentation. However training of deep networks is time consuming and requires large training datasets. A network trained in one dataset could be applied to another task or dataset through transfer learning and retraining. As
Sercan Sunetci, Hasan F. Ates
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Statistical image modeling for semantic segmentation
IEEE Transactions on Consumer Electronics, 2010Semantic image segmentation (SIS) is one of the most crucial steps toward image understanding. In this paper, a novel framework to enable SIS is proposed by modeling images automatically. The statistical model for an image is automatically obtained by using a finite mixture model to approximate the underlying class distributions of image pixels.
Zhongjie Zhu, Yuer Wang, Gangyi Jiang
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Semantic segmentation of high-resolution images
Science China Information Sciences, 2017Image semantic segmentation is a research topic that has emerged recently. Although existing approaches have achieved satisfactory accuracy, they are limited to handling low-resolution images owing to their large memory consumption. In this paper, we present a semantic segmentation method for high-resolution images. First, we downsample the input image
Juhong Wang, Bin Liu, Kun Xu 0003
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Synthesizing Training Images for Semantic Segmentation
2018Semantic segmentation is one of the key problems in the computer vision area. Recently, Convolutional Neural Networks (CNNs) have yielded a significant performance for the semantic segmentation task. However, CNNs require a sufficient amount of annotated training images, which is challenging since massive human labour is needed.
Yunhui Zhang +3 more
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