A Context-Based Region Labeling Approach for Semantic Image Segmentation

Athanasiadis, Thanos; Mylonas, Phivos; Avrithis, Yannis

doi:10.1007/11930334_17

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Athanasiadis et al [1] propose a method which involves region labeling and image segmentation simultaneously. Bi-Layer sparse coding is presented by Liu et al [10], where they construct the semantic regions by selecting atomic pathes in each layer and propagating the labels from images to regions.…”

Section: Related Workmentioning

confidence: 99%

Image Region Labeling by Exploring Contextual Information of Visual Spatial and Semantic Concepts

Chan

Zhu

et al. 2014

Advances in Multimedia Information Processing – PCM 2014

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Abstract. Region Labeling is to automatically assign semantic labels to the corresponding image regions. Most of the previous works focus on exploiting low level visual features, particularly visual spatial contextual information, to address the problem. However, very few work explore high level semantic information of the whole image to deal with the problem. In this paper, we propose a new region labeling approach by integrating both visual spatial and semantic contextual information into a unified model. In our method, region labeling is regarded as a multi-class classification problem. For each semantic concept, we train a Conditional Random Field (CRF) model respectively. It consists of both the region grid sub-graph and the co-occurred semantic label sub-graph. In our model, the integration of the two kinds of contextual information brings reinforcement effect on the improvement of region labeling. The experiments are conducted on two commonly used benchmark datasets and the experimental results show that our method achieves the best performance compared with the strong baselines and the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Image Region Labeling by Exploring Contextual Information of Visual Spatial and Semantic Concepts

Chan

Zhu

et al. 2014

Advances in Multimedia Information Processing – PCM 2014

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“…Although, there are many interesting works in the region-growing topic, segmentation approaches in that group are still very different comparatively to the human vision system. The main reason is that the human vision uses high-level knowledge relative to the image [8]. In fact, by using only low-level features, these segmentation approaches can lead to merge regions that do not belong to the same object at semantic level.…”

Section: Introductionmentioning

confidence: 99%

A Combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation

Chaibou

Kalti

Solaiman

et al. 2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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We present in this paper an image segmentation approach that combines a fuzzy semantic region classification and a context based region-growing. Input image is first over-segmented. Then, prior domain knowledge is used to perform a fuzzy classification of these regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of using low-level features and consequently to remedy to the problem of the semantic gap. Each oversegmented region is represented by a vector giving its corresponding membership degrees to the different thematic labels and the whole image is therefore represented by a Regions Partition Matrix. The segmentation is achieved on this matrix instead of the image pixels through two main phases: focusing and propagation. The focusing aims at selecting seeds regions from which information propagation will be performed. The propagation phase allows to spread toward others regions and using fuzzy contextual information the needed knowledge ensuring the semantic segmentation. An application of the proposed approach on mammograms shows promising results.

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Conditional Random Fields for Image Region Labeling with Global Observation

Lin

Chan

et al. 2013

Lecture Notes in Computer Science

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A Context-Based Region Labeling Approach for Semantic Image Segmentation

Cited by 6 publications

References 12 publications

Image Region Labeling by Exploring Contextual Information of Visual Spatial and Semantic Concepts

Image Region Labeling by Exploring Contextual Information of Visual Spatial and Semantic Concepts

A Combined Approach Based on Fuzzy Classification and Contextual Region Growing to Image Segmentation

Conditional Random Fields for Image Region Labeling with Global Observation

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