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17th European Conference on Computer Vision (ECCV)

作者： Liu, Qin Zheng, Meng Planche, Benjamin Karanam, Srikrishna Chen, Terrence Niethammer, Marc Wu, Ziyan Univ North Carolina Chapel Hill Chapel Hill NC USA United Imaging Intelligence Cambridge MA USA

ISBN: (纸本)9783031200670;9783031200687

The goal of click-based interactive image segmentation is to obtain precise object segmentation masks with limited user interaction, i.e., by a minimal number of user clicks. Existing methods require users to provide all the clicks: by first inspecting the segmentation mask and then providing points on mislabeled regions, iteratively. We ask the question: can our model directly predict where to click, so as to further reduce the user interaction cost? To this end, we propose PseudoClick, a generic framework that enables existing segmentation networks to propose candidate next clicks. These automatically generated clicks, termed pseudo clicks in this work, serve as an imitation of human clicks to refine the segmentation mask. We build PseudoClick on existing segmentation backbones and show how the click prediction mechanism leads to improved performance. We evaluate PseudoClick on 10 public datasets from different domains and modalities, showing that our model not only outperforms existing approaches but also demonstrates strong generalization capability in cross-domain evaluation. We obtain new state-of-the-art results on several popular benchmarks, e.g., on the Pascal dataset, our model significantly outperforms existing state-of-the-art by reducing 12.4% number of clicks to achieve 85% IoU.

关键词： Click imitation interactive image segmentation Pseudo click

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Superpixels Based interactive image segmentation Algorithm

Superpixels Based Interactive Image Segmentation Algorithm

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2013 International Conference on Information,Business and Education Technology(ICIBET 2013)

作者： LONG Jian-wu SHEN Xuan-jing ZANG Hui CHEN Hai-peng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

ISBN: (纸本)9781629932101

A superpixels based interactive image segmentation algorithm is proposed in this paper. Firstly the initial segmentation is obtained by MeanShift algorithm, and then a graph is built using pre-segmented regions as nodes, finally min-cut/maxflow algorithm is implemented for global solution. In this process, each region is represented by a color histogram and Bhattacharyya coefficient is chosen to calculate the similarity between any two regions. Extensive experiments are performed and the results show that the presented algorithm obtains much more satisfactory segmentation results with less user interaction and less comsuming time than MSRM algorithm.

关键词： interactive image segmentation graph cut mean shift

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A dual-stream framework guided by adaptive Gaussian maps for interactive image segmentation®

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KNOWLEDGE-BASED SYSTEMS 2021年 223卷 107033-107033页

作者： Ding, Zongyuan Wang, Tao Sun, Quansen Cui, Qiongjie Chen, Fuhua Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiao Ling Wei St Nanjing 210094 Peoples R China West Liberty Univ Dept Phys Sci & Math 208 Univ DrColl Union Box 190 West Liberty WV 26074 USA

Efficiently embedding user-annotation is a key issue for deep interactive image segmentation. In this paper, we propose a dual-stream framework guided by adaptive Gaussian maps for interactive image segmentation. The network architecture consists of two branches: a traditional fully convolutional neural network that produces coarse segmentation results, and an interactive shape stream that produces target boundary information by integrating user-annotations. The boundary information combined with user-intention can suppress the feature response outside the target, which boosts the performance of coarse segmentation. Additionally, we develop an adaptive Gaussian map with distinct variances to encode user-annotations, which promotes sensitivity to details by adaptively adjusting the affected region of the annotations. Specifically, when the distance between two interactions is smaller than a threshold, we shrink the Gaussian variance of these interactions to enhance the perception of details, thereby improving the segmentation performance of the details. Extensive experiments show that our algorithm effectively reduces the burden of user interaction under the restriction of clear target boundaries and excels at fine-tuning the details with the adaptive Gaussian maps. (C) 2021 Published by Elsevier B.V.

关键词： interactive image segmentation Dual-stream neural network Gated convolution Adaptive Gaussian map

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Seed extraction using superpixel-based SLIC for interactive image segmentation

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JOURNAL OF ELECTRONIC IMAGING 2022年第1期31卷 013018-013018页

作者： Lin, Kaibin Li, Qiaoliang Wang, Guoqun Hunan Normal Univ Sch Math & Stat MOE LCSM Changsha Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang Peoples R China

For complex images, interactive image segmentation can achieve better segmentation performance than other approaches. However, many classical interactive segmentation methods are sensitive to the locations and number of seed pixels. We introduce a seed extraction method to address this issue. First, we propose a robust seed measure to select and mark the seed pixels that cover the whole image. We use the center pixel of each superpixel as a seed. Then, each center pixel is labeled by our seed measure. Second, we propose a relabeling method for addressing isolated seeds. The extracted seeds can be used to locate the initial object area and can serve as the inputs of other interactive segmentation methods to reduce their dependence on user interaction. Extensive experiments on the Berkeley, GrabCut, and MSRA1000 datasets demonstrate the effectiveness of our seed extraction method. (C) 2022 SPIE and IS&T

关键词： interactive image segmentation seed extraction simple linear iterative clustering superpixel

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An interactive image segmentation Method Based on Multi-Level Semantic Fusion

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SENSORS 2023年第14期23卷 6394-6394页

作者： Zou, Ruirui Wang, Qinghui Wen, Falin Chen, Yang Liu, Jiale Du, Shaoyi Yuan, Chengzhi Longyan Univ Sch Phys & Mech & Elect Engn Longyan 364012 Peoples R China Jiaotong Univ Sch Software Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Univ Rhode Isl Dept Mech Ind & Syst Engn Kingston RI 02881 USA

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the accurate separation of the target object from its background based on user annotation information. However, existing interactive object segmentation methods struggle to effectively leverage such information to guide object-segmentation models. To address these challenges, this paper proposes an interactive image-segmentation technique for static images based on multi-level semantic fusion. Our method utilizes user-guidance information both inside and outside the target object to segment it from the static image, making it applicable to both 2D and 3D sensor data. The proposed method introduces a cross-stage feature aggregation module, enabling the effective propagation of multi-scale features from previous stages to the current stage. This mechanism prevents the loss of semantic information caused by multiple upsampling and downsampling of the network, allowing the current stage to make better use of semantic information from the previous stage. Additionally, we incorporate a feature channel attention mechanism to address the issue of rough network segmentation edges. This mechanism captures richer feature details from the feature channel level, leading to finer segmentation edges. In the experimental evaluation conducted on the PASCAL Visual Object Classes (VOC) 2012 dataset, our proposed interactive image segmentation method based on multi-level semantic fusion demonstrates an intersection over union (IOU) accuracy approximately 2.1% higher than the currently popular interactive image segmentation method in static images. The comparative analysis highlights the improved performance and effectiveness of our method. Furthermore, our method exhibits potential ap

关键词： interactive image segmentation attention cross-stage feature aggregation model complexity

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Separability-based intelligent scissors for interactive image segmentation

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2007年第1期E90D卷 137-144页

作者： Suetake, Noriaki Uchino, Eiji Hirata, Kanae Yamaguchi Univ Fac Sci Dept Phys Biol & Informat Yamaguchi 7538512 Japan FLSI Iizuka Fukuoka 8200067 Japan

Intelligent scissors is an interactive image segmentation algorithm which allows a user to select piece-wise globally optimal contour segment corresponding to a desired object boundary. However, the intelligent scissors is too sensitive to a noise and texture patterns in an image since it utilizes the gradient information concerning the pixel intensities. This paper describes a new intelligent scissors based on the concept of the separability in order to improve the object boundary extraction performance. The effectiveness of the proposed method has been confirmed by some experiments for actual images acquired by an ordinary digital camera.

关键词： interactive image segmentation intelligent scissors separability magic wand active contour model

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Rethinking interactive image segmentation: Feature space annotation

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PATTERN RECOGNITION 2022年 131卷

作者： Bragantini, Jordao Falcao, Alexandre X. Najman, Laurent Univ Estadual Campinas Lab Image Data Sci Campinas Brazil Univ Gustave Eiffel Equipe A3SI LIGM ESIEE Champs Sur Marne France

Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space projection. This strategy is in stark contrast to existing interactive segmentation methodologies, which perform annotation in the image domain. We show that feature space annotation achieves com-petitive results with state-of-the-art methods in foreground segmentation datasets: iCoSeg, DAVIS, and Rooftop. Moreover, in the semantic segmentation context, it achieves 91.5% accuracy in the Cityscapes dataset, being 74.75 times faster than the original annotation procedure. Further, our contribution sheds light on a novel direction for interactive image annotation that can be integrated with existing method-ologies. The supplementary material presents video demonstrations. Code available at https://***/ LIDS- UNICAMP/rethinking- interactive- image-segmentation . (c) 2022 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Data annotation interactive machine learning Feature space annotation

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Fast and robust interactive image segmentation in bilateral space with reliable color modeling and higher order potential

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JOURNAL OF ELECTRONIC IMAGING 2021年第3期30卷 033018-033018页

作者： Gui, Yan Zhou, Bingqiang Xiong, Daming Wei, Wu Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Tran Changsha Peoples R China

We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random field energy on the vertices of the bilateral grid that can be solved efficiently using a standard graph cut label assignment. To achieve this, we introduce reliable color models to distinguish the foreground and background despite the presence of extremely difficult cases and a higher-order potential to encourage spatial consistency in segmentation. We conduct comprehensive experiments on three standard interactive segmentation datasets, MSRA 10K, IIS, and PASCAL VOC 2012 segmentation validation set. The results show that the proposed method achieves competitive performance compared with state-of-the-art methods while making the current system efficient in terms of speed. (C) 2021 SPIE and IS&T

关键词： interactive image segmentation bilateral space reliable color modeling higher-order potential

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interactive color image segmentation via iterative evidential labeling

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INFORMATION FUSION 2014年第1期20卷 292-304页

作者： Chen, Yin Cremers, Armin B. Cao, Zhiguo Huazhong Univ Sci & Technol Sch Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Univ Bonn Inst Comp Sci 3 D-53117 Bonn Germany

We develop an interactive color image segmentation method in this paper. This method makes use of the conception of Markov random fields (MRFs) and D-S evidence theory to obtain segmentation results by considering both likelihood information and priori information under Bayesian framework. The method first uses expectation maximization (EM) algorithm to estimate the parameter of the user input regions, and the Bayesian information criterion (BIC) is used for model selection. Then the beliefs of each pixel are assigned by a predefined scheme. The result is obtained by iteratively fusion of the pixel likelihood information and the pixel contextual information until convergence. The method is initially designed for two-label segmentation, however it can be easily generalized to multi-label segmentation. Experimental results show that the proposed method is comparable to other prevalent interactive image segmentation algorithms in most cases of two-label segmentation task, both qualitatively and quantitatively. (C) 2014 Elsevier B.V. All rights reserved.

关键词： interactive image segmentation Markov random fields (MRFs) Dempster-Shafer's (DS) theory of evidence Bayesian information criterion (BIC) Information fusion

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SUPERCUT: AN ACCURATE AND EFFECTIVE interactive image segmentation ALGORITHM

SUPERCUT: AN ACCURATE AND EFFECTIVE INTERACTIVE IMAGE SEGMEN...

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IEEE International Conference on image Processing

作者： Qingsong Zhu Ling Shao Zhan Song Yaoqin Xie Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Electronic and Electrical Engineering University of Sheffield Sheffield UK The Chinese University of Hong Kong Hong Kong China

ISBN: (纸本)9781479923427

The task of interactive image segmentation has attracted a significant attention in recent years. The ultimate goal is to extract an object with as few user interactions as possible. In this paper, we present SUPERCUT, a novel interactive algorithm for foreground object extraction and segmentation in images. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to efficiently presegment the original image into super-pixels with precise boundary. Secondly, a Bayes decision theory is introduced to model and cluster the super-pixels so as to obtain an initial effective classification of super-pixels. To achieve a more accurate object segmentation result, a boundary refinement using interactive rectangle box with GMM learning is adopted. Experimental results on a benchmark data set show that the proposed framework is highly effective and can accurately segment a wide variety of natural images with ease.

关键词： interactive image segmentation Mean shift algorithm Graph cut interactive box images image segmentation interactive Object segmentation Decision Theory algorithms original image Boundary boundary layer extraction of object

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