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

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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Complex Network Construction for interactive image segmentation Using Particle Competition and Cooperation: A New Approach 20th

Complex Network Construction for Interactive Image Segmentat...

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Passerini, Jefferson Antonio Ribeiro Breve, Fabricio Sao Paulo State Univ BR-13506900 Rio Claro SP Brazil

ISBN: (纸本)9783030587994;9783030587987

In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist's intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49% when classifying pixels with the proposed model while the reference model had an error rate of 3.14%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.

关键词： interactive image segmentation Machine learning Semi-supervised learning Particle competition and cooperation

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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 image segmentation Using Semi-transparent Wearable Glasses

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IEEE TRANSACTIONS ON MULTIMEDIA 2018年第1期20卷 208-223页

作者： Kim, Kyumok Jung, Seung-Won Dongguk Univ Dept Multimedia Engn Seoul 100715 South Korea

Since it is difficult to automatically and precisely extract an object of interest, interactive image segmentation techniques exploit user-provided segmentation seeds. In previous interactive segmentation applications, the segmentation seeds are typically provided by mouse clicks or finger touches. In this paper, the segmentation of an object is studied from the scene that the user sees through semi-transparent wearable glasses. In this application scenario, a front-view camera is used to obtain the segmentation seeds from the user's fingertip position. In particular, two segmentation methodologies called transparent segmentation and semi-transparent segmentation are considered to determine an effective segmentation scheme for the wearable glasses. Extensive user studies are performed to evaluate the user preferences and the segmentation accuracies of the two methodologies.

关键词： Hand gesture interactive image segmentation semi-transparent glasses wearable device

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interactive image segmentation on Multiscale Appearances

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IEEE ACCESS 2018年 6卷 67732-67741页

作者： He, Kun Wang, Dan Tong, Miao Zhang, Xu Sichuan Univ Sch Comp Sci Chengdu 610065 Sichuan Peoples R China Harbin Univ Sci & Technol Dept Math Harbin 150080 Heilongjiang Peoples R China

interactive segmentation algorithms based on graph cuts can extract the foreground successfully from a simple scene. However, they are ineffective for complex-scene images. To improve the segmentation performance, we propose an interactive segmentation algorithm, which combines the segmentation and the multiscale smoothing into a unified model. This model consists of the segmentation and the smoothing. The segmentation relies on the multiscale appearances, which depend on the smoothing. In the smoothing part, the total variation is used to preserve the geometric shape of the foreground and captures different scale edges and appearances for segmentation. Combining the multiscale edges and appearances, we propose a novel Gibbs energy functional for segmentation. The exact global minima of the energy can be found by jointing the image smoothing and the optimization of segmentation. In this algorithm, the smoothing motivates that the foreground could be detected easily from a proper scale. Experimental results on the BSD300 data set and Weizmann horse's database indicate that, compared with the existing interactive segmentation algorithms, the proposed algorithm provides competitive performance in terms of segmentation accuracy.

关键词： interactive image segmentation multiscale appearance multiscale edge graph cut

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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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Volumetric memory network for interactive medical image segmentation

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MEDICAL image ANALYSIS 2023年 83卷 102599页

作者： Zhou, Tianfei Li, Liulei Bredell, Gustav Li, Jianwu Unkelbach, Jan Konukoglu, Ender Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Hosp Zurich Dept Radiat Oncol Zurich Switzerland

Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet clinically acceptable accuracy, thus typically require further refinement. To this end, we propose a novel Volumetric Memory Network, dubbed as VMN, to enable segmentation of 3D medical images in an interactive manner. Provided by user hints on an arbitrary slice, a 2D interaction network is firstly employed to produce an initial 2D segmentation for the chosen slice. Then, the VMN propagates the initial segmentation mask bidirectionally to all slices of the entire volume. Subsequent refinement based on additional user guidance on other slices can be incorporated in the same manner. To facilitate smooth human-in-the-loop segmentation, a quality assessment module is introduced to suggest the next slice for interaction based on the segmentation quality of each slice produced in the previous round. Our VMN demonstrates two distinctive features: First, the memory-augmented network design offers our model the ability to quickly encode past segmentation information, which will be retrieved later for the segmentation of other slices;Second, the quality assessment module enables the model to directly estimate the quality of each segmentation prediction, which allows for an active learning paradigm where users preferentially label the lowest-quality slice for multi-round refinement. The proposed network leads to a robust interactive segmentation engine, which can generalize well to various types of user annotations (e.g., scribble, bounding box, extreme clicking). Extensive experiments have been conducted on three public medical image segmentation datasets (i.e., MSD, KiTS19, CVC-ClinicDB), and the results clearly confirm the superiority of our approach in comparison with state-of-the-art segmentation models. The code is made publicly available at https://***/0liliulei/Mem3D.

关键词： interactive image segmentation Memory-augmented network Attention fully convolutional network Deep learning

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Adaptive appearance separation for interactive image segmentation based on Dense CRF

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IET image PROCESSING 2019年第1期13卷 142-151页

作者： Peng, Zili Li, Qiaoliang Hunan Normal Univ Key Lab High Performance Comp & Stochast Informat Minist Educ China Coll Math & Comp Sci Changsha 410081 Hunan Peoples R China

interactive segmentation has recently become a hot topic for its wide application. The authors propose an efficacious appearance separation model for interactive binary segmentation, which incorporates the difference of foreground and background colour models and the difference of corresponding geodesic models into the popular densely connected conditional random field (Dense CRF) framework. The proposed method can adaptively set relevant parameter values in this framework according to the characteristics of target images in a per-image manner, therefore, it gets rid of the dependence on specific datasets. After accomplishing a mean-field inference, the authors are able to get satisfactory results without the time-consuming parameter learning process and multiple iterative optimisations. Overall, the proposed approach is highly efficient and mitigates the contradiction between accuracy and segmentation efficiency. In addition, the proposed approach reduces the efforts of scribble-style interaction from users. The experimental results on three famous datasets show that the proposed method is superior to the other five new algorithms released in recent years regarding accuracy, and is faster than or close to them in runtime.

关键词： image colour analysis learning (artificial intelligence) image segmentation adaptive appearance separation interactive image segmentation Dense CRF interactive segmentation hot topic wide application efficacious appearance separation model interactive binary segmentation foreground background colour models corresponding geodesic models popular densely connected conditional random field framework relevant parameter values target images per-image manner mean-field inference time-consuming parameter segmentation efficiency scribble-style interaction

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A fully convolutional two-stream fusion network for interactive image segmentation

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NEURAL NETWORKS 2019年 109卷 31-42页

作者： Hu, Yang Soltoggio, Andrea Lock, Russell Carter, Steve Loughborough Univ Loughborough Leics England ICE Agcy Poole Dorset England

In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactive image segmentation. The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution. The TSLFN includes two distinct deep streams followed by a fusion network. The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result. The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution. We conduct comprehensive experiments on four benchmark datasets. The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods.(1) (c) 2018 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Fully convolutional network Two-stream network

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Dense learning by high dimensional SOMs composed of input-output fusion vectors for interactive image segmentation

Dense learning by high dimensional SOMs composed of input-ou...

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International Workshop on Advanced image Technology (IWAIT)

作者： Takizawa, Hotaka Univ Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan

This study proposes an interactive image segmentation method based on high dimensional self organizing maps (SOMs). The proposed method was applied to gray-scale and color images. The experimental results demonstrated... 详细信息

ISBN: (数字)9781510627741

ISBN: (纸本)9781510627741

关键词： Dense learning High dimensional self organizing maps Input-output fusion vectors interactive image segmentation

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