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Error-tolerant label prior for interactive image segmentation

INFORMATION SCIENCES 2020年 538卷 384-395页

作者： Wang, Tao Qi, Shengzhe Ji, Zexuan Sun, Quansen Fu, Peng Ge, Qi Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210023 Peoples R China

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint, which however ignore the label prior estimation. This paper mainly focuses on solving the label prior estimation problem when error marks appear. The prior probability is generally defined as the matching degree between pixels and the cluster centers of marked pixels. Incorrect interaction leads to the formation of incorrect clusters. Therefore, a reliability learning model is constructed in this paper by 1) assigning smaller weighting factors to incorrect clusters, 2) assigning larger weighting factors to correct clusters with higher matching degree. Accurate label prior probability can be obtained by the weighted averaging. Then referring to the existing methods, an error-tolerant segmentation model is designed as a ratio energy function, which can both overcome the hard constraint and the label prior limitation with error marks. Experiments on the challenging Berkeley segmentation data set and Microsoft GrabCut database demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： interactive image segmentation Error tolerance Reliability learning Graph cut

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PVPUFormer: Probabilistic Visual Prompt Unified Transformer for interactive image segmentation

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IEEE TRANSACTIONS ON image PROCESSING 2024年 33卷 6455-6468页

作者： Zhang, Xu Yang, Kailun Lin, Jiacheng Yuan, Jin Li, Zhiyong Li, Shutao Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Natl Engn Res Ctr Robot Visual Percept & Control T Sch Robot Changsha 410082 Peoples R China

Integration of diverse visual prompts like clicks, scribbles, and boxes in interactive image segmentation significantly facilitates users' interaction as well as improves interaction efficiency. However, existing studies primarily encode the position or pixel regions of prompts without considering the contextual areas around them, resulting in insufficient prompt feedback, which is not conducive to performance acceleration. To tackle this problem, this paper proposes a simple yet effective Probabilistic Visual Prompt Unified Transformer (PVPUFormer) for interactive image segmentation, which allows users to flexibly input diverse visual prompts with the probabilistic prompt encoding and feature post-processing to excavate sufficient and robust prompt features for performance boosting. Specifically, we first propose a Probabilistic Prompt-unified Encoder (PPuE) to generate a unified one-dimensional vector by exploring both prompt and non-prompt contextual information, offering richer feedback cues to accelerate performance improvement. On this basis, we further present a Prompt-to-Pixel Contrastive (P2C) loss to accurately align both prompt and pixel features, bridging the representation gap between them to offer consistent feature representations for mask prediction. Moreover, our approach designs a Dual-cross Merging Attention (DMA) module to implement bidirectional feature interaction between image and prompt features, generating notable features for performance improvement. A comprehensive variety of experiments on several challenging datasets demonstrates that the proposed components achieve consistent improvements, yielding state-of-the-art interactive segmentation performance. Our code is available at https://***/XuZhang1211/PVPUFormer.

关键词： image segmentation Visualization Probabilistic logic image coding Vectors Transformers Feature extraction Encoding Merging Robots interactive image segmentation transformer visual prompt contrastive loss

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CGAN: lightweight and feature aggregation network for high-performance interactive image segmentation

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VISUAL COMPUTER 2024年第3期40卷 2203-2217页

作者： Yan, Gui Zhengyan, Zhang Zhihua, Chen Chuang, Zhang Jin, Zhang Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha 410114 Hunan Peoples R China East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

In the task of interactive image segmentation, user interactions about the object of interest are accepted to predict the segmentation mask. Recent works have demonstrated state-of-the-art results by using either backpropagating refinement or iterative training scheme, which are computationally expensive. In this paper, we propose a novel method for interactive image segmentation using conditional generative adversarial networks to enforce higher-order consistency in the segmentation, without extra post-processing during inference. Concretely, we develop a new segmentation network which integrates three different modules by providing global contextual information and attentions and conducting feature fusions across multiple layers. This allows the segmentation network to learn strong object representations and predict more accurate segmentations. We then employ a fully convolutional discriminator to detect and correct higher-order inconsistency between the predictions of the segmentation network and the ground truth label maps. To achieve this, we optimize an objective function that combines the conventional segmentation loss with the adversarial loss of the adversarial term. We train our network on the Pascal VOC 2012 and MS COCO 2017 datasets and conduct comprehensive experiments on four benchmark datasets. Experimental results show that the adversarial training to the network architecture has improved segmentation results over state-of-the-art methods, while making the current system efficient in terms of speed.

关键词： interactive image segmentation Conditional generative adversarial network Adversarial learning Feature aggregation network Higher-order consistency

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Global Manifold Learning for interactive image segmentation

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3239-3249页

作者： Wang, Tao Ji, Zexuan Yang, Jian Sun, Quansen Fu, Peng Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

This paper presents an interactive image segmen-tation algorithm, in which the segmentation problem is formulated as a global manifold learning process. Based on the principle that the label of each element depends on the influence of all the elements in the image, we extend the conventional local neighborhood or the long range regional relationships to the global relationships over the whole image. A probabilistic framework is established to measure the global effect of each element on all other elements. Based on two different manifold learning styles, the semi-global manifold learning (SGML) and the fully-global manifold learning (FGML) algorithms are proposed to capture the global geometry structure of the data manifold. SGML learns the intrinsic effects of each element separately, equivalent to a matrix diffusion process on an affinity graph. FGML learns the intrinsic effects of all elements together, equivalent to a label pair diffusion process on a higher-order tensor product graph. The global manifold learning helps to overcome the low contrast, weak boundary and texture problems. Extensive experiments on three public interactive segmentation datasets demonstrate the superior performance of the proposed algorithms both in accuracy and efficiency.

关键词： image segmentation Manifolds Estimation Tensile stress Sun Probabilistic logic Geometry interactive image segmentation global effects manifold learning label propagation

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Robust interactive image segmentation using structure-aware labeling

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 79卷 90-100页

作者： Oh, Changjae Ham, Bumsub Sohn, Kwanghoon Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea

interactive image segmentation has remained an active research topic in image processing and graphics, since the user intention can be incorporated to enhance the performance. It can be employed to mobile devices which now allow user interaction as an input, enabling various applications. Most interactive segmentation methods assume that the initial labels are correctly and carefully assigned to some parts of regions to segment. Inaccurate labels, such as foreground labels in background regions for example, lead to incorrect segments, even by a small number of inaccurate labels, which is not appropriate for practical usage such as mobile application. In this paper, we present an interactive segmentation method that is robust to inaccurate initial labels (scribbles). To address this problem, we propose a structure-aware labeling method using occurrence and co-occurrence probability (OCP) of color values for each initial, label in a unified framework. Occurrence probability captures a global distribution of all color values within each label, while co-occurrence one encodes a local distribution of color values around the label. We show that nonlocal regularization together with the OCP enables robust image segmentation to, inaccurately assigned labels and alleviates a small-cut problem. We analyze theoretic relations of our approach to other segmentation methods. Intensive experiments with synthetic and manual labels show that our approach outperforms the state of the art. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Co-occurrence probability Graph-based optimization

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Implementation and analysis of quantum-classical hybrid interactive image segmentation algorithm based on quantum annealer

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QUANTUM INFORMATION PROCESSING 2024年第8期23卷 1-22页

作者： Wang, Kehan Wang, Shuang Chen, Qinghui Qiao, Xingyu Ma, Hongyang Qiu, Tianhui Qingdao Univ Technol Sch Sci Qingdao 266033 Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266033 Peoples R China

With the development of computer vision and digital image processing technology, image segmentation has become an important part of various image processing and image analysis. Since interactive segmentation can obtain more accurate results than automatic segmentation, the most representative Graph Cuts has gradually become a popular method in image segmentation. However, this algorithm has two significant disadvantages. On the one hand, if the background is complex or very similar to the foreground, the accuracy will be low;on the other hand, the algorithm is slow and the iteration process is complicated. To improve it, this paper proposes a new image segmentation algorithm based on quantum annealing and Graph Cuts. The algorithm beds the classical interactive image segmentation problem into a quantum optimization algorithm and obtains ideal image segmentation results on the D-Wave quantum annealer. Meanwhile, it is compared with the other three methods. Compared with MATLAB, the segmentation results are more beautiful, with an average precision higher than 5.27% and an average recall higher than 5.43%;the quantum annealing time is always lower than the simulated annealing time;and the success probability is more than twice that of the quantum approximate optimization algorithm. Therefore, it is concluded that this method is superior.

关键词： Quantum annealing interactive image segmentation D-Wave quantum annealer QUBO Graph Cuts

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Grouping Boundary Proposals for Fast interactive image segmentation

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IEEE TRANSACTIONS ON image PROCESSING 2024年 33卷 793-808页

作者： Liu, Li Chen, Da Shu, Minglei Cohen, Laurent D. Qilu Univ Technol Shandong Artificial Intelligence Inst Shandong Acad Sci Jinan 250014 Peoples R China Univ Paris 09 PSL Res Univ CNRS CEREMADEUMR 7534 F-75016 Paris France

Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a segmentation strategy cannot take into account the connectivity of the image edge features, increasing the risk of shortcut problem, especially in the case of complicated scenario. In this work, we introduce a new image segmentation model based on the minimal geodesic framework in conjunction with an adaptive cut-based circular optimal path computation scheme and a graph-based boundary proposals grouping scheme. Specifically, the adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once. The boundary proposals are comprised of precomputed image edge segments, providing the connectivity information for our segmentation model. These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them. Experimental results show that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.

关键词： interactive image segmentation circular paths boundary proposal grouping fast marching method Eikonal equation

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Alpha cut for interactive image segmentation of thin and elongated objects

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IET image PROCESSING 2019年第11期13卷 1951-1959页

作者： Tran, Nam H. Seo, Dongsun Woo, Dongmin Won, Yongyuk Tu, Hieu T. Myongji Univ Elect Dept 116 Myongji Ro Yongin 449728 Gyeonggi Do South Korea EY Solut LLP TAS Dept 1 Raffles Quay Singapore 018960 Singapore

Shrinking bias problem is a practical limitation of graph-cut-based methods for binary image segmentation. The detail parts with thin elongated object are not well preserved in graph cut minimisation as a result. The authors propose to use structure transferring to overcome the problem with an expanded L2 norm of colour difference. They also show that the structure-transferring output can be integrated into graph cuts as a counter-balance to shrinking problem. Experimental results show that the proposed alpha-cutting technique is effective and efficient in improving segmentation of thin elongated objects.

关键词： image segmentation minimisation graph theory interactive systems alpha-cutting technique elongated object interactive image segmentation graph-cut-based methods binary image segmentation graph cut minimisation expanded L2 norm structure-transferring output thin objects shrinking bias problem colour difference

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Robust interactive image segmentation Using Convex Active Contours

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IEEE TRANSACTIONS ON image PROCESSING 2012年第8期21卷 3734-3743页

作者： Thi Nhat Anh Nguyen Cai, Jianfei Zhang, Juyong Zheng, Jianmin Danang Univ Technol Da Nang 6038047 Vietnam Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

The state-of-the-art interactive image segmentation algorithms are sensitive to the user inputs and often unable to produce an accurate boundary with a small amount of user interaction. They frequently rely on laborious user editing to refine the segmentation boundary. In this paper, we propose a robust and accurate interactive method based on the recently developed continuous-domain convex active contour model. The proposed method exhibits many desirable properties of an effective interactive image segmentation algorithm, including robustness to user inputs and different initializations, the ability to produce a smooth and accurate boundary contour, and the ability to handle topology changes. Experimental results on a benchmark data set show that the proposed tool is highly effective and outperforms the state-of-the-art interactive image segmentation algorithms.

关键词： Convex active contour digital image editing interactive image segmentation

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User-Friendly interactive image segmentation Through Unified Combinatorial User Inputs

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IEEE TRANSACTIONS ON image PROCESSING 2010年第9期19卷 2470-2479页

作者： Yang, Wenxian Cai, Jianfei Zheng, Jianmin Luo, Jiebo Nanyang Technol Univ Sch Comp Engn Singapore Singapore Eastman Kodak Co Rochester NY 14650 USA

One weakness in the existing interactive image segmentation algorithms is the lack of more intelligent ways to understand the intention of user inputs. In this paper, we advocate the use of multiple intuitive user inputs to better reflect a user's intention. In particular, we propose a constrained random walks algorithm that facilitates the use of three types of user inputs: 1) foreground and background seed input, 2) soft constraint input, and 3) hard constraint input, as well as their combinations. The foreground and background seed input allows a user to draw strokes to specify foreground and background seeds. The soft constraint input allows a user to draw strokes to indicate the region that the boundary should pass through. The hard constraint input allows a user to specify the pixels that the boundary must align with. Our proposed method supports all three types of user inputs in one coherent computational framework consisting of a constrained random walks and a local editing algorithm, which allows more precise contour refinement. Experimental results on two benchmark data sets show that the proposed framework is highly effective and can quickly and accurately segment a wide variety of natural images with ease.

关键词： Digital image editing interactive image segmentation multiple user inputs random walks algorithm

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