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SwipeCut: interactive segmentation via Seed Grouping

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第9期30卷 2959-2970页

作者： Chen, Ding-Jie Chen, Hwann-Tzong Chang, Long-Wen Acad Sinica Inst Informat Sci Taipei 115 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu 300 Taiwan

interactive image segmentation algorithms rely on the user to provide annotations as the guidance. When the task of interactive segmentation is performed on a small touchscreen device, the requirement of providing precise annotations could be cumbersome to the user. We design a new interaction mechanism that actively queries seeds for guiding the user to label. Our method enforces sparsity and diversity criteria on the selection of query seeds, and at each round of interaction, the user is only presented with a small number of informative query seeds that are far apart from each other. Therefore, the user merely has to swipe through on the ROI-relevant query seeds for checking which ones of the query seeds are inside the region of interest (ROI). This kind of interaction should be easy since those gestures are commonly used on a touchscreen. As a result, we are able to derive a user-friendly interaction mechanism for annotation on small touchscreen devices. The performance of our algorithm is evaluated on six publicly available datasets. The evaluation results show that our algorithm achieves high segmentation accuracy, with short computational time and less user feedback.

关键词： image segmentation Proposals Labeling Task analysis Performance evaluation Indexes Electronic mail interactive image segmentation touchscreen seed proposals

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Multi-Branch U-Net for interactive segmentation

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 974-978页

作者： Li, Zhicheng Wang, Tao Mei, Chun Pei, Zhenyu Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

In deep learning-based interactive segmentation, one of the key issues is how to effectively incorporate label prior into the network. The sparsity of clicks and the difference between images and label information limit the compatibility of user intent embedding in the deep long-path network. This letter aims to explore more effective embedding strategy of label prior in the neural network, and proposes a multi-branch U-net architecture for the interactive segmentation task. A principled fusion manner is adopted to progressively enhance the semantics of features based on the layer-by-layer promotion of the label prior guidance. Vast experiments on the popular GrabCut, Berkeley, DAVIS and SBD datasets verified the effectiveness of the proposed method.

关键词： image annotation interactive image segmentation multi-branch U-net label 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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Learning interactive multi-object segmentation through appearance embedding and spatial attention

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IET image PROCESSING 2022年第10期16卷 2722-2737页

作者： Gui, Yan Zhou, Bingqiang Zhang, Jianming Sun, Cheng Xiang, Lingyun Zhang, Jin 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 Hunan Normal Univ Sch Math & Stat Changsha Peoples R China

Deep learning approaches to interactive image segmentation are typically formulated as a binary labeling problem. A model trained to make predictions within a fixed set of labels (i.e., foreground and background labels) cannot be used to directly predict the binary masks of multiple objects of interest, which greatly limits its flexibility and adaptivity. The use of different classes of clicks as input is opted for and the first end-to-end learning model for multi-object segmentation, based on a new designed neural network, is developed. The network consists of a visual feature extractor, a recurrent attention module and a dynamic segmentation head, extracts user click-adapted appearance embedding features and spatial attention features, and then learns to transform this information into a segmentation of multiple objects. It is also proposed to train the network using a joint loss function, taking the embedding learning into account for segmentation. Comprehensive experiments are conducted on three benchmark datasets to demonstrate the effectiveness of the proposed method. It performs favorably against state-of-the-art approaches on the multiple object segmentation task, for example, with 0.15 s per image, 0.06 s per object and mean IoU & F1 score of 84.90% on Pascal VOC 2012 validation set. It is further shown that the method can be used in numerous vision applications such as image recoloring and colorization.

关键词： spatial attention features colorization visual feature extractor interactive multiobject segmentation recurrent neural nets foreground background labels embedding learning joint loss function learning (artificial intelligence) multiple objects feature extraction binary masks image segmentation deep learning (artificial intelligence) image recoloring end-to-end learning model flexibility user click-adapted appearance embedding features recurrent attention module interactive image segmentation object detection multiple object segmentation task designed neural network dynamic segmentation head binary labeling problem

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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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Learning a Mahalanobis distance metric for data clustering and classification

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PATTERN RECOGNITION 2008年第12期41卷 3600-3612页

作者： Xiang, Shiming Nie, Feiping Zhang, Changshui Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

Distance metric is a key issue in many machine learning algorithms. This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links. As one kind of side information, a must-link indicates the pair of the two data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Given must-link and cannot-link information, our goal is to learn a Mahalanobis distance metric. Under this metric, we hope the distances of point pairs in must-links are as small as possible and those of point pairs in cannot-links are as large as possible. This task is formulated as a constrained optimization problem, in which the global optimum can be obtained effectively and efficiently. Finally, some applications in data clustering, interactive natural image segmentation and face pose estimation are given in this paper. Experimental results illustrate the effectiveness of our algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： distance metric learning Mahalanobis distance global optimization data clustering interactive image segmentation face pose estimation

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Privacy preservation network with global-aware focal loss for interactive Personal Visual Privacy Preservation

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NEUROCOMPUTING 2024年 602卷

作者： Xiao, Zhiqiang Lin, Jiacheng Chen, Jiajun Fu, Haolong Li, Yifan Yuan, Jin Li, Zhiyong Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Sch Robot Changsha 410082 Peoples R China

Existing visual privacy preservation methods often encounter the challenge of over-protection, which usually fails to accurately protect a specified person in an image. This paper introduces a novel task called "interactive Personal Visual Privacy Preservation (IPVPP)", with the objective of safeguarding a desired individual in an image based on user-click prompts. We propose a new framework, Privacy Preservation Network (PPNet) with Global-aware Focal Loss, tailored for IPVPP task. To address the sample imbalance issue in IPVPP, we present a Global-aware Focal Loss (GFL), which introduces global and local difficulty balance weights to dynamically adjust the overall decision boundary, thereby mitigating the sample imbalance problem for better optimization of models. Furthermore, to tackle the insufficient fusion between click and visual features, we propose a Click- image Fusion (CIF) module, which effectively emphasizes the interaction between click and image features, providing robust query inputs to the model. We finally employ generative adversarial networks to ensure the reusability of privacy images, striking a balance between privacy preservation and image value. Experimental results on the DAVIS dataset demonstrate the effectiveness of PPNet, achieving improvements measured in NoC 90, IS, SSIM, F-1, and DIR, with 0.22, 0.19, 0.009, 4.23, and 2.15 improvements, respectively.

关键词： Global-aware focal loss Click-image fusion Visual privacy preservation interactive image segmentation

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Personal Fixations-Based Object segmentation With Object Localization and Boundary Preservation

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IEEE TRANSACTIONS ON image PROCESSING 2021年 30卷 1461-1475页

作者： Li, Gongyang Liu, Zhi Shi, Ran Hu, Zheng Wei, Weijie Wu, Yong Huang, Mengke Ling, Haibin 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 Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based Object segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an Object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://***/MathLee/OLBPNet4PFOS.

关键词： image segmentation Object segmentation Task analysis Visualization Visual systems Measurement image edge detection Personal fixations interactive image segmentation object localization boundary preservation

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USER DRIVEN SPARSE POINT-BASED image segmentation

USER DRIVEN SPARSE POINT-BASED IMAGE SEGMENTATION

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IEEE International Conference on image Processing

作者： Sachin Meena Kannappan Palaniappan Guna Seetharaman Department of Computer Science University of Missouri-Columbia US Naval Research Laboratory

ISBN: (纸本)9781467399623

Reducing the amount of user driven input for interactive image segmentation enables faster and more precise foreground extraction of objects. A sparse collection of labeled seed points sampled over image regions can be quickly provided by the user using a few mouse clicks. Seed points are used for training an Elastic Body Spline classifier mapping function. We evaluate the efficiency and accuracy of user defined point inputs compared to fully manual boundary drawing that can be time consuming and automatic image segmentation methods that may not have sufficient accuracy. We show that using an average of just 8 labeled pixels (i.e. sparse set of seed points) the proposed EBS foreground-background thresholding method can achieve 90 percent accuracy compared to manual ground truth on the Berkeley BSDS500 benchmark.

关键词： interactive image segmentation Seed points Semi-supervised regression Elastic body splines image segmentation Consumers images region of an image mouse-click Seeds manual elastic bodies

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Mining Semantic Correlations Between Mispredictions and Corrections for interactive Semantic segmentation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期PP卷 8036-8048页

作者： Gao, Yutong Lang, Congyan Liu, Fayao Foo, Chuan-Sheng Cao, Yuanzhouhan Sun, Lijuan Wei, Yunchao Beijing Jiaotong Univ Sch Comp & Informat Technol Key Lab Big Data & Artificial Intelligence Transpo Minist Educ Beijing 100044 Peoples R China Minzu Univ China Sch Informat Engn Key Lab Ethn Language Intelligent Anal & Secur Gov Minist Educ Beijing 100081 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China ASTAR Inst Infocomm Res Singapore 138632 Singapore Beijing Jiaotong Univ Sch Comp Sci & Informat Technol Beijing 100044 Peoples R China Beihang Univ Intellectual Property Informat Serv Ctr Beijing 100044 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

interactive semantic segmentation pursues high-quality segmentation results at the cost of a small number of user clicks. It is attracting more and more research attention for its convenience in labeling semantic pixel-level data. Existing interactive segmentation methods often pursue higher interaction efficiency by mining the latent information of user clicks or exploring efficient interaction manners. However, these works neglect to explicitly exploit the semantic correlations between user corrections and model mispredictions, thus suffering from two flaws. First, similar prediction errors frequently occur in actual use, causing users to repeatedly correct them. Second, the interaction difficulty of different semantic classes varies across images, but existing models use monotonic parameters for all images which lack semantic pertinence. Therefore, in this article, we explore the semantic correlations existing in corrections and mispredictions by proposing a simple yet effective online learning solution to the above problems, named correction-misprediction correlation mining (CM2). Specifically, we leverage the correction-misprediction similarities to design a confusion memory module (CMM) for automatic correction when similar prediction errors reappear. Furthermore, we measure the semantic interaction difficulty by counting the correction-misprediction pairs and design a challenge adaptive convolutional layer (CACL), which can adaptively switch different parameters according to interaction difficulties to better segment the challenging classes. Our method requires no extra training besides the online learning process and can effectively improve interaction efficiency. Our proposed CM2 achieves state-of-the-art results on three public semantic segmentation benchmarks.

关键词： Semantics Correlation Semantic segmentation Training Task analysis Annotations Switches interactive image segmentation semantic image segmentation

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