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Adaptive fusion with multi-scale features for interactive image segmentation

APPLIED INTELLIGENCE 2021年第8期51卷 5610-5621页

作者： Ding, Zongyuan Wang, Tao Sun, Quansen Wang, Hongyuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiao Ling Wei St Nanjing 210094 Peoples R China Changzhou Univ Sch Informat Sci & Engn 1 Ge Hu Zhong Lu Changzhou 213164 Jiangsu Peoples R China

Multi-scale features are usually utilized to improve the performance of interactive image segmentation, however, they have varying leverages over the result of segmentation, for example, thinner segmentation results could be achieved by pixel-level features, but sensitive to image noise, and superpixel-level features could provide the semantic perception of the object, but easily lead to over-segmentations. Therefore, we propose an interactive image segmentation algorithm by adaptive fusion with multi-scale features (AFMSF). It intends on combining the multi-scale information adaptively for the segmentation via learning the influence coefficients of multi-scale features. First, multi-scale superpixel layers are generated by controlling the size of superpixels. Based on features of this multi-scale information, the similarity matrices and label priors with pixel-superpixel levels are then obtained. A fusion with diffusion strategy is designed to build the energy function by combining these multi-cues. Finally, the influence coefficient of each scale and the labeling are updated with each other until convergence. The algorithm we proposed is robust to diverse circumstances of objects, the experimental results on public interactive image segmentation datasets Graz, LHI, and MSRC validate the superior performance of the proposed method.

关键词： interactive image segmentation Semi-supervised learning Multi-scale features Fusion with diffusion

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interactive image segmentation via Superpixel Pairs Probabilistic Diffusion 11

Interactive Image Segmentation via Superpixel Pairs Probabil...

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11th International Conference on Graphics and image Processing (ICGIP)

作者： Xia, Yu Wang, Tao Ji, Zexuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781510635241

This paper introduces a new interactive image segmentation approach based on global pairwise relationship. Many conventional interactive image segmentation methods only consider local relationship of neighboring pixels or unary probability of pixels, which results in the sensitivity to seeds. To overcome this drawback, we utilizes the pixel-pairwise relationship to obtain the global pairwise relationship of pixels. The constructed global binary probability is used to estimate the labels of pixels. In order to improve the computational efficiency, we further replace pixels with superpixels and use the binary global relationship of superpixels for image segmentation. Our method makes full use of global binary information and has stronger robustness to limited seeds information. The superior performances of our method are demonstrated in the experiments on the Berkeley segmentation dataset and Microsoft GrabCut database.

关键词： interactive image segmentation superpixel pairs probabilistic diffusion global information

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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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A Clicking Strategy Inspired by Inter-Individual Variation for interactive image segmentation 23

A Clicking Strategy Inspired by Inter-Individual Variation f...

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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Shuofeng Zhao Chunzhi Gu Jun Yu Chao Zhang University of Fukui Japan Toyohashi University of Technology Japan Niigata University Japan

ISBN: (纸本)9798400716225

Click-based interactive segmentation is a fundamental task in computer vision that allows user clicks to refine the results. Existing works typically focus on developing powerful segmentation models, yet sparsely treating the clicking method itself. In this study, we propose a novel clicking strategy that specifically aims to reflect the inter-individual variations of different humans during training to improve segmentation results. Our method consists of three steps. In particular, we first apply the erosion operation on the ground-truth segmentation mask with different parameter settings to generate multiple eroded masks. These eroded masks are then regarded as possible hypotheses of users’ interested regions. Then, we randomly select one mask from the hypotheses to simulate an arbitrary users’ behavior. By next solving the visual center of the selected mask, the training clicks are eventually obtained via randomly sampling from the visual center region. In essence, our key idea is to cast multiple eroded regions as the potentially diverse users’ interests, and include the resulting stochasticity into the model training for better generality. We directly adopt an existing segmentation backbone and incorporate our clicking strategy in the training to show the effectiveness of our method. Experimental results on five datasets generally demonstrate that our method contributes to state-of-the-art segmentation performance.

关键词： Click strategies Human Visual Psychology Inter-Individual variation interactive image segmentation segmentation

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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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A survey of recent interactive image segmentation methods

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Computational Visual Media 2020年第4期6卷 355-384页

作者： Hiba Ramadan Chaymae Lachqar Hamid Tairi Department of Informatics Faculty of Sciences Dhar El MahrazUniversity of Sidi Mohamed Ben AbdellahFez 30000Morocco Faculty of Medicine and Pharmacy University of Sidi Mohamed Ben AbdellahFez 30000Morocco

image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation(IIS), often referred to as foreground–background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets,evaluation metrics, and available resources in the field of IIS.

关键词： interactive image segmentation user interaction label propagation deep learning superpixels

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interactive image segmentation using label propagation through complex networks

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 123卷 18-33页

作者： Breve, Fabricio Sao Paulo State Univ Inst Geosci & Exact Sci UNESP BR-13506900 Rio Claro SP Brazil

interactive image segmentation is a topic of many studies in image processing. In a conventional approach, a user marks some pixels of the object(s) of interest and background, and an algorithm propagates these labels to the rest of the image. This paper presents a new graph-based method for interactive segmentation with two stages. In the first stage, nodes representing pixels are connected to their k-nearest neighbors to build a complex network with the small-world property to propagate the labels quickly. In the second stage, a regular network in a grid format is used to refine the segmentation on the object borders. Despite its simplicity, the proposed method can perform the task with high accuracy. Computer simulations are performed using some real-world images to show its effectiveness in both two classes and multi-classes problems. It is also applied to all the images from the Microsoft GrabCut dataset for comparison, and the segmentation accuracy is comparable to those achieved by some state-of-the-art methods, while it is faster than them. In particular, it outperforms some recent approaches when the user input is composed only by a few "scribbles" draw over the objects. Its computational complexity is only linear on the image size at the best-case scenario and linearithmic in the worst case. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Label propagation Complex networks

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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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interactive image segmentation using geodesic appearance overlap graph cut

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SIGNAL PROCESSING-image COMMUNICATION 2019年 78卷 159-170页

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

image segmentation is a fundamental step in many applications such as image editing, medical image analysis and processing. It is quite common to use graph cuts for image segmentation in recent years. Tang, Gorelick, Veksler, and Boykov proposed a new image segmentation model which uses the appearance overlap on unnormalized histograms and graph cut framework. Their model is highly effective for interactive segmentation but is prone to isolated points. To avoid that problem, we propose an effective interactive image segmentation method, that is appropriately incorporating geodesic distance information, appearance overlap information, and edge information together into the well-known graph-cut framework. Rather than a simple union of these information, the respective strengths of each information term can be tuned adaptively in our method. We utilize the user's scribbles to obtain the estimated foreground/background color models via fast kernel density estimation, and then get the appearance overlap intricateness according to the inferred color models. By taking comprehensive advantage of the geodesic distance and the global appearance overlap color clues, our method requires less user effort and achieves higher accuracy of segmentation than the latest interactive segmentation techniques, such as Geodesic Graph Cut, GrabCut in One Cut, Semi-Supervised Normalized Cuts, and Convexity Shape Prior for Binary segmentation, as shown in our experiments.

关键词： Graph-cut Geodesic Appearance overlap interactive image segmentation

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interactive image segmentation using region-based fully connected CRF models with graph cut based fine tuning

Interactive image segmentation using region-based fully conn...

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4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Wang Dan Hu Guoqing Lyu Chengzhi South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143903

interactive image segmentation has gained much popularity since it can enhance the segmentation performance with human interactions. In this paper, we propose a novel framework that can effectively estimate the binary labels based on Conditional Random Field (CRF) models combining region-level information with pixels-level information. The user scribbles are treated as soft constraints. We use pixel-layer with local CRF and region-layer with fully connected CRF to model the graph. Then we combine the pixel information and region information together to calculate the pixels' likelihood term. In contrast to using relations between the pixel-layer and the region-layer during the probability computation, we calculate the probabilities of pixels and regions separately to reduce computational complexity. After the likelihood computation, a graph cut based model is used to refine the pixel-level label predictions of the image in order to avoid label inconsistency in regions and capture the boundary details. Due to the less relationship between layers and utilizing the refinement, the proposed model can provide high accuracy and user inputs insensitive results. Our experiments show that the proposed approach can efficiently and accurately segment the images compared with other state of art methods with only a few scribbles. Graph cut based fine tuning improves our segmentation accuracy and our method needs less computational cost than traditional multi-layer-based methods.

关键词： interactive image segmentation region-based model fully connected CRF graph cut

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