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interactive image segmentation combining global seeding and sparse local reconstruction

PATTERN ANALYSIS AND APPLICATIONS 2025年第2期28卷 1-23页

作者： Long, Jianwu Liu, Yuanqin Zhang, Kaixin Chen, Shuang Luo, Qi Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China

Seed segmentation methods are highly regarded for their effectiveness in processing complex images, user-friendliness, and compatibility with graph-based representations. However, these methods often depend on intricate computational tools, leading to issues such as poor image contour adherence and incomplete seed propagation. To address these limitations, this paper proposes an interactive framework that integrates global seed information with sparse local linear reconstruction regularization (GSSR). In this framework, a Gaussian mixture model is firstly employed to construct a flow of global seed information, establishing connections between pixel points and yielding more complete segmented objects. Additionally, the L-p(0 < p <= 1) norm is utilized to constrain the sparse local reconstruction term, facilitating the generation of sparse boundaries. An iterative process based on the Alternating Direction Method of Multipliers (ADMM) is developed to solve the L1 regularization term, which is then generalized for the L-p problem through reweighting. We conduct a comprehensive comparison on the BSD dataset, CVC-ClinicDB datasets and two publicly available MSRC datasets with different labeling schemes. Extensive experimental validation demonstrates that the proposed method outperforms existing *** source code and datasets are openly available at: https://***/choppy-water/GSSR.

关键词： interactive image segmentation Sparse regularity Local linear reconstruction Global seed diffusion model

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FusionNet for interactive image segmentation 7th

FusionNet for Interactive Image Segmentation

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Wu, Enyi Shi, Qingxuan Wang, Kanglin Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Peoples R China Hebei Univ Hebei Machine Vis Engn Res Ctr Baoding 071002 Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Despite the advancements in neural network technologies driving interactive image segmentation forward, challenges persist, especially concerning segmentation ambiguities caused by overlapping or visually similar objects against complex backgrounds, as well as intricate object boundaries. Addressing these challenges, we introduce FusionNet, focusing on effective feature fusion. Firstly, the Hierarchical Context Fusion Module aids in grasping holistic structures and multi-scale contextual information of target objects. Secondly, the Attention Feature Fusion Module captures more representative feature expressions. This design empowers FusionNet to capture details and contextual relationships better, thereby enhancing segmentation accuracy. For fine-grained boundary details, we propose the Local Correction Module, refining local mask details meticulously. This module initially focuses on information around newly clicked areas, employing discriminative correction feedback for enhanced detail processing accuracy. Rigorous experimentations on datasets like SBD, DAVIS, GrabCut, and Berkeley validate our model's effectiveness, with segmentation results strongly supporting the superiority of our approach.

关键词： interactive image segmentation Feature fusion Attention mechanism

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SPT: Sequence Prompt Transformer for interactive image segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cheng, Senlin Sun, Haopeng Xie, Tao Zhao, Hangyue Chen, Yiqiang Xu, Bolei Li, XiaoBo Ant Group Hangzhou China BAIDU Beijing China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Top-k Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20KSeq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets. © 2025 IEEE.

关键词： computer vision interactive image segmentation

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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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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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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 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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interactive image segmentation by matching attributed relational graphs

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PATTERN RECOGNITION 2012年第3期45卷 1159-1179页

作者： Noma, Alexandre Graciano, Ana B. V. Cesar, Roberto M., Jr. Consularo, Luis A. Bloch, Isabelle Univ Sao Paulo Rua Matao Inst Math & Stat BR-05508090 Sao Paulo Brazil Tribunal Super Eleitoral SAS BR-70096900 Brasilia DF Brazil Telecom ParisTech CNRS LTCI F-75013 Paris France

A model-based graph matching approach is proposed for interactive image segmentation. It starts from an over-segmentation of the input image, exploiting color and spatial information among regions to propagate the labels from the regions marked by the user-provided seeds to the entire image. The region merging procedure is performed by matching two graphs: the input graph, representing the entire image;and the model graph, representing only the marked regions. The optimization is based on discrete search using deformed graphs to efficiently evaluate the spatial information. Note that by using a model-based approach, different interactive segmentation problems can be tackled: binary and multi-label segmentation of single images as well as of multiple similar images. Successful results for all these cases are presented, in addition to a comparison between our binary segmentation results and those obtained with state-of-the-art approaches. An implementation is available at http://***/. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Matching attributed relational graphs Deformed graph Spatial configuration

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interactive image segmentation Using Adaptive Constraint Propagation

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IEEE TRANSACTIONS ON image PROCESSING 2016年第3期25卷 1301-1311页

作者： Jian, Meng Jung, Cheolkon Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

In this paper, we propose interactive image segmentation using adaptive constraint propagation (ACP), called ACP Cut. In interactive image segmentation, the interactive inputs provided by users play an important role in guiding image segmentation. However, these simple inputs often cause bias that leads to failure in preserving object boundaries. To effectively use this limited interactive information, we employ ACP for semi-supervised kernel matrix learning which adaptively propagates the interactive information into the whole image, while successfully keeping the original data coherence. Moreover, ACP Cut adopts seed propagation to achieve discriminative structure learning and reduce the computational complexity. Experimental results demonstrate that the ACP Cut extracts foreground objects successfully from the background and outperforms the state-of-the-art methods for interactive image segmentation in terms of both effectiveness and efficiency.

关键词： Adaptive constraint propagation discriminative structure learning interactive image segmentation seed propagation

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