Image annotation by modeling Supporting Region Graph

Guo, Qing; Li, Ning; Yang, Yu-Bin; Wu, Guorong

doi:10.1007/s10489-013-0473-1

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The common approach is to use a graphical inference model based on the Conditional Random Field (CRF) since various contextual information among image regions can easily be integrated into the CRF framework [15]- [21]. However, training and predicting with high-order CRFs are computationally expensive, and thus, only very limited neighborhood relations can be employed in the graph structure [22], [23]. More importantly, another pressing issue is when different features are combined without considering their individual (or collective) importance for every class.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs

Zand

Doraisamy

Halin

et al. 2016

IEEE Trans. on Image Process.

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Semantic image segmentation is a fundamental yet challenging problem, which can be viewed as an extension of the conventional object detection with close relation to image segmentation and classification. It aims to partition images into non-overlapping regions that are assigned predefined semantic labels. Most of the existing approaches utilize and integrate low-level local features and high-level contextual cues, which are fed into an inference framework such as, the conditional random field (CRF). However, the lack of meaning in the primitives (i.e., pixels or superpixels) and the cues provides low discriminatory capabilities, since they are rarely object-consistent. Moreover, blind combinations of heterogeneous features and contextual cues exploitation through limited neighborhood relations in the CRFs tend to degrade the labeling performance. This paper proposes an ontology-based semantic image segmentation (OBSIS) approach that jointly models image segmentation and object detection. In particular, a Dirichlet process mixture model transforms the low-level visual space into an intermediate semantic space, which drastically reduces the feature dimensionality. These features are then individually weighed and independently learned within the context, using multiple CRFs. The segmentation of images into object parts is hence reduced to a classification task, where object inference is passed to an ontology model. This model resembles the way by which humans understand the images through the combination of different cues, context models, and rule-based learning of the ontologies. Experimental evaluations using the MSRC-21 and PASCAL VOC'2010 data sets show promising results.

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Section: Introductionmentioning

confidence: 99%

Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs

Zand

Doraisamy

Halin

et al. 2016

IEEE Trans. on Image Process.

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Adaptive image annotation: refining labels according to contents and relations

Xiao

Chen

Zhang

et al. 2022

Neural Comput & Applic

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Interactive image segmentation via cascaded metric learning

Shi

Yang

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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Deep Metric Learning (DML) is helpful in computer vision tasks. In this paper, we firstly introduce DML into image co-segmentation. We propose a novel Triplet loss for Image Segmentation, called IS-Triplet loss for short, and combine it with traditional image segmentation loss. Different from the general DML task which learns the metric between pictures, we treat each pixel as a sample, and use their embedded features in high-dimensional space to form triples, then we tend to force the distance between pixels of different categories greater than of the same category by optimizing IS-Triplet loss so that the pixels from different categories are easier to be distinguished in the high-dimensional feature space. We further present an efficient triple sampling strategy to make a feasible computation of IS-Triplet loss. Finally, the IS-Triplet loss is combined with 3 traditional image segmentation losses to perform image segmentation. We apply the proposed approach to image co-segmentation and test it on the SBCoseg dataset and the Internet dataset. The experimental result shows that our approach can effectively improve the discrimination of pixels' categories in high-dimensional space and thus help traditional loss achieve better performance of image segmentation with fewer training epochs.

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Image annotation by modeling Supporting Region Graph

Cited by 3 publications

References 16 publications

Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs

Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs

Adaptive image annotation: refining labels according to contents and relations

Interactive image segmentation via cascaded metric learning

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