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IEEE International Conference on Multimedia and Expo (ICME)

作者： Forte, Marco Price, Brian Cohen, Scott Xu, Ning Pitie, Francois Trinity Coll Dublin Dublin Ireland Adobe Inc San Jose CA USA

ISBN: (纸本)9781728113319

interactive object cutout tools are the cornerstone of the image editing workflow. Algorithms that can reduce the number of interactions are clearly valuable. Recent deep-learning based interactive segmentation algorithms are capable of rough binary selections with a handful of clicks, yet, they tend to plateau once this rough selection has been reached. In this work, we interpret this plateau as an inability of the algorithm to precisely leverage each user interaction. We introduce a novel interactive architecture and a training scheme that are both tailored to better exploit the user input at higher numbers of clicks. Comprehensive experiments support our approach, and our network achieves state of the art performance.

关键词： interactive image segmentation Semantic segmentation image Matting Deep Learning

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A Novel Level Set Model for image segmentation with interactive Label Regularization Term 2nd

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2nd CCF Chinese Conference on Computer Vision (CCCV)

作者： Tan, Huang Chen, Ming Li, Qiaoliang Qu, Shaojun Hunan Normal Univ Coll Math & Comp Sci Minist Educ China Key Lab High Performance Comp & Stochast Informat Changsha 410081 Hunan Peoples R China

ISBN: (纸本)9789811073052;9789811073045

We propose an interactive level set segmentation method with a novel user's label regularization term. This new edge-based model can force the evolution of level set function to follow the hard constraints given by the user and effectively solve the problem that user can't extract some specified object in the image due to the local optimum. The new regularization term is constructed by multiplication of the hard constraints and the level set function. Since the regularization term we defined only works on the pixels labeled by the user, the evolution of level set function can be affected accurately by user's label for foreground and background. Experimental results are provided to demonstrate the efficiency and accuracy of the new model. Our method can make the segmentation accurately reflect the user's label. The new method supports the real-time feedback in the segmentation process. We also analyzed the weight of the regularization term, and the best weight of the regularization term is provided.

关键词： interactive image segmentation Real-time feedback Level set Regularization term

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Constrained fixation point based segmentation via deep neural network

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NEUROCOMPUTING 2019年 368卷 180-187页

作者： Li, Gongyang Liu, Zhi Shi, Ran Wei, Weijie Shanghai Univ Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

It is an explicit mode to use the clicking points by the mouse in the interactive image segmentation, while an implicit interaction mode is to use the fixation points from the eye-tracking device. Both modes can provide a series of points. Inspired by the similarity between these two interaction modes, we propose a novel human visual system (HVS) based neural network for transferring the constrained fixation point based segmentation to the clicking point based interactive segmentation. Briefly speaking, the sequence of information transmission and processing in our model is RGB image, VGG-16 backbone, LGN-like module (LGNL) and ConvLSTM block, which correspond to the pathway of stimulus transmission and processing, i.e. stimulus, retina, lateral geniculate nucleus (LGN) and visual cortex in the HVS. First, the RGB image is fed to the VGG-16 backbone to obtain the multiple-layer feature maps. Then the LGNL is adopted to effectively incorporate edge-aware features and semantic features from different layers of the VGG-16 backbone in multiple resolutions, so as to produce rich contextual features. Finally, with the guidance of the fixation density map transformed from the fixation points, the output feature maps of LGNL are utilized to generate the segmentation map via a stack of ConvLSTM blocks in a coarse-to-fine manner. Comprehensive experiments demonstrate that the proposed HVS based neural network achieves a higher segmentation performance and outperforms seven state-of-the-art methods, and prove that the transfer from constrained fixation points to clicking points is reasonable and valid. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Human visual system Object segmentation interactive image segmentation Fixation point Deep neural network

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Análise de características em imagens digitais na tarefa de segmentação interativa

Análise de características em imagens digitais na tarefa d...

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作者： Faccio, Thiago Amorim de Universidade Estadual Paulista

A tarefa de segmentação de imagem está presente em diversos programas e aplicativos ao alcance de qualquer usuário de tecnologia. Apesar disso, pouco se fala sobre o funcionamento desta ferramenta, isto é, como ela é realmente executada quando levado em consideração não simplesmente o ponto de vista gráfico, mas também o processo matemático e computacional por trás desse tipo de tarefa. Visto isso, este trabalho faz uma breve abordagem sobre a matemática e as metodologias que regem técnicas de Processamento Digital de imagens, com foco em métodos de Segmentação Interativa, e busca por meio do estudo de diferentes métodos clássicos, alcançar melhorias nos resultados deste tipo de tarefa. Tem-se como objetivo obter maior precisão na realização de segmentações através da análise de novas características extraídas da imagem, as quais são adotadas como elementos de entrada para o cálculo dos pesos do grafo associado à segmentação do método das Coordenadas de Laplace. A proposta analisou diferentes composições de features (bandas) de realce dos objetos e bordas de uma imagem, sendo essas bandas resultantes de técnicas como, por exemplo, modelos de mistura gaussiana, o algoritmo expectation–maximization, detector de bordas Canny, funções de distância local, entre outras. Assim, almejou-se atingir melhores resultados tanto do ponto de vista quantitativo, a partir de métricas de aferição de partições tais como o Rand Index (RI), Variation ou Information (VoI) e o Dice Coefficient (DICE), como também em termos qualitativos, seja para imagens com poucas marcações de entrada fornecidas pelo usuário ou com riqueza das *** segmentation is an omnipresent task in many programs and applications commonly used by technology users. Despite its systematic use, the segmentation task has several issues not properly exploited in terms of the mathematical background, i.e., how the segmentation works not only in the visual interpretation aspect, but also considering the mathematical

关键词： Segmentação interativa de imagens Análise de features Método das coordenadas de laplace Processamento digital de imagens Detector de bordas interactive image segmentation Features analysis Laplacian coordinates Digital image processing Edge detec

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interactive image segmentation With Multiple Linear Reconstructions in Windows

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IEEE TRANSACTIONS ON MULTIMEDIA 2011年第2期13卷 342-352页

作者： Xiang, Shiming Pan, Chunhong Nie, Feiping Zhang, Changshui Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Tsinghua Univ Dept Automat State Key Lab Intelligent Technol & Syst TNList Beijing 100084 Peoples R China

This paper proposes an algorithm for interactive image segmentation. The task is formulated as a problem of graph-based transductive classification. Specifically, given an image window, the color of each pixel in it will be reconstructed linearly with those of the remaining pixels in this window. The optimal reconstruction weights will be kept unchanged to linearly reconstruct their class labels. The label reconstruction errors are estimated in each window. These errors are further collected together to develop a learning model. Then, the class information about the user specified foreground and background pixels are integrated into a regularization framework. Under this framework, a globally optimal labeling is finally obtained. The computational complexity is analyzed, and an approach for speeding up the algorithm is presented. Comparative experimental results illustrate the validity of our algorithm.

关键词： Comparative study interactive image segmentation multiple linear reconstructions in windows

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Global graph diffusion for interactive object extraction

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INFORMATION SCIENCES 2018年 460卷 103-114页

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

Many interactive image segmentation methods design energy functions based on the local relationship of neighboring pixels, which is insufficient at capturing the abundant information of an image, and these methods are susceptible to many problems, such as sensitivity to seeds and under-segmentation of boundaries. To solve these problems, this paper explores utilizing the global relationship for interactive image segmentation. To effectively obtain the global information of the image, we first propose a robust affinity diffusion (RAD) method to propagate the local affinity graph. Compared with the existing diffusion based approaches, the advantage of RAD is that it can converge to an effective limit value, which makes the diffusion process more computationally efficient and easier to control. The segmentation model is then constructed based on this convergent global graph. To efficiently utilize the global information, the energy function is designed by the multiplication of the global affinity matrix and a prior probability vector. The use of global information can significantly improve the segmentation performance. Experiments on challenging data sets demonstrate that RAD can obtain better results than state-of-the-art methods. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Color image interactive image segmentation Global information Graph diffusion

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interactive 1-bit feedback segmentation using transductive inference

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MACHINE VISION AND APPLICATIONS 2018年第4期29卷 617-631页

作者： Chen, Ding-Jie Chen, Hwann-Tzong Chang, Long-Wen Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan

This paper presents an effective algorithm, interactive 1-bit feedback segmentation using transductive inference (FSTI), that interactively reasons out image segmentation. In each round of interaction, FSTI queries the user one superpixel for acquiring 1-bit user feedback to define the label of that superpixel. The labeled superpixels collected so far are used to refine the segmentation and generate the next query. The key insight is treating the interactive segmentation as a transductive inference problem, and then suppressing the unnecessary queries via an intrinsic-graph-structure derived from transductive inference. The experiments conducted on five publicly available datasets show that selecting query superpixels concerning the intrinsic-graph-structure is helpful to improve the segmentation accuracy. In addition, an efficient boundary refinement is presented to improve segmentation quality by revising the misaligned boundaries of superpixels. The proposed FSTI algorithm provides a superior solution to the interactive image segmentation problem is evident.

关键词： interactive image segmentation Transductive inference Intrinsic-graph-structure

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Online background subtraction with freely moving cameras using different motion boundaries

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image AND VISION COMPUTING 2018年第Aug.期76卷 76-92页

作者： Sugimura, Daisuke Teshima, Fumihiro Hamamoto, Takayuki Tsuda Univ Dept Comp Sci Tokyo 1878577 Japan Tokyo Univ Sci Dept Elect Engn Tokyo 1258585 Japan

We propose a method for online background subtraction from a successive-frame video captured using a freely moving camera. Our method exploits a technique of interactive image segmentation with seeds (the subsets of pixels marked as "foreground" and "background"). The key novelty of our method is to automatically estimate the seeds by exploiting two different motion boundaries that are respectively computed using the magnitude and direction of the flow field. The magnitude of flow field is likely to be useful in differentiating the foreground and background motions when the moving objects and the camera make a movement towards the same direction. In contrast, the direction of flow field helps in discriminating the observed motions when the amount of displacement of the moving objects and the camera is the same. By adaptively exploiting the advantages of these different motion boundaries, our method enables to estimate the reliable foreground/background seeds. With the estimated seeds, our method performs accurate background subtraction even when the complex camera movements (e.g., large pan-tilt-zoom, rotation) are made. Our experiments demonstrate the effectiveness of our method using public dataset and other real image sequences. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Online background subtraction Freely moving camera interactive image segmentation Seeds estimation Motion boundary

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A Kernel Method with Manifold Regularization for interactive segmentation 1

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36th National Conference of Theoretical Computer Science (NCTCS)

作者： Chen, Haohao Zhu, En Liu, Xinwang Zhang, Junnan Yin, Jianping Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Dongguan Univ Technol Guangzhou 523000 Guangdong Peoples R China

ISBN: (数字)9789811327124

ISBN: (纸本)9789811327124;9789811327117

interactive segmentation has been successfully applied to various applications such as image editing, computer vision, image identification. Most of existing methods require interaction for each single image segmentation, which costs too much labor interactions. To address this issue, we propose a kernel based semi-supervised learning framework with manifold regularization for interactive image segmentation in this paper. Specifically, by manifold regularization, our algorithm makes similar superpixel pair bearing the same label. Moreover, the learned classifier on one single image is directly used to similar images for segmentation. Extensive experimental results demonstrate the effectiveness of the proposed approach.

关键词： interactive image segmentation Semi-supervised learning Manifold regularization

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Object Oriented Structure from Motion: Can a Scribble Help? 13

Object Oriented Structure from Motion: Can a Scribble Help?

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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Rahal, Rahaf Asmar, Daniel Shammas, Elie Ghanem, Bernard Amer Univ Beirut Dept Mech Engn Beirut Lebanon King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (纸本)9789897583063

The concept of anywhere anytime scanning of 3D objects is very appealing. One promising solution to extract structure is to rely on a monocular camera to perform, what is well-known as Structure from Motion (SfM). Despite the significant progress achieved in SfM, the structures that are obtained are still below par the quality of reconstruction obtained through laser scanning, especially when objects are kept as part of their background. This paper looks into the idea of treating points in the scene non-uniformly, in an attempt to give more weight to the objects of interest. The system presented utilizes a minimal user interaction, in the form of a scribble, to segment the pertinent objects from different views and focus the reconstruction on them, leading to what we call Object Oriented SfM (OOSfM). We test the effect of OOSfM on the reconstruction of specific objects by formulating the bundle adjustment (BA) step in three novel manners. Our proposed system is tested on several real and synthetic datasets, and results of the different formulations of BA presented are reported and compared to the conventional (vanilla) SfM pipeline results. Experiments show that keeping the background points actually improves the reconstructed objects of interest.

关键词： Structure from Motion interactive image segmentation Object Reconstruction

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