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Accelerated Learning-Based interactive image segmentation Using Pairwise Constraints

IEEE TRANSACTIONS ON image PROCESSING 2014年第7期23卷 3057-3070页

作者： Sourati, Jamshid Erdogmus, Deniz Dy, Jennifer G. Brooks, Dana H. Northeastern Univ Dept Elect & Comp Engn Biomed Signal Proc Imaging Reasoning & Learning G Boston MA 02115 USA

Algorithms for fully automatic segmentation of images are often not sufficiently generic with suitable accuracy, and fully manual segmentation is not practical in many settings. There is a need for semiautomatic algorithms, which are capable of interacting with the user and taking into account the collected feedback. Typically, such methods have simply incorporated user feedback directly. Here, we employ active learning of optimal queries to guide user interaction. Our work in this paper is based on constrained spectral clustering that iteratively incorporates user feedback by propagating it through the calculated affinities. The original framework does not scale well to large data sets, and hence is not straightforward to apply to interactive image segmentation. In order to address this issue, we adopt advanced numerical methods for eigen-decomposition implemented over a subsampling scheme. Our key innovation, however, is an active learning strategy that chooses pairwise queries to present to the user in order to increase the rate of learning from the feedback. Performance evaluation is carried out on the Berkeley segmentation and Graz-02 image data sets, confirming that convergence to high accuracy levels is realizable in relatively few iterations.

关键词： interactive image segmentation affinity propagation pairwise querying active learning spectral clustering

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Robust interactive image segmentation via graph-based manifold ranking

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Computational Visual Media 2015年第3期1卷 183-195页

作者： Hong Li Wen Wu Enhua Wu Department of Computer and Information Science University of Macao Chinese Academy of Sciences

interactive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction,a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation,and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.

关键词： interactive image segmentation graph structure graph edge weights manifold ranking relevance inference

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Seed growing for interactive image segmentation using SVM classification with geodesic distance

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ELECTRONICS LETTERS 2017年第1期53卷 22-23页

作者： Park, S. Lee, H. S. Kim, J. Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

In an interactive image segmentation, the quantity of a user-given seed is known to affect the segmentation accuracy. In this Letter, we propose a seed-growing method expanding the quantity of a seed to reduce the bias of the given seed and improve the segmentation accuracy. To grow the given seed, a supervised classification framework with geodesic distance features is proposed. From a single input image, a support vector machine (SVM) classifier is trained on the seed superpixels of an input image. Other non-seed superpixels are then classified into object, background and non-seed regions by the trained classifier. In experiments, the proposed method showed promising results by improving the segmentation accuracy of existing segmentation methods in public benchmark datasets.

关键词： image segmentation support vector machines image classification differential geometry learning (artificial intelligence) interactive image segmentation SVM classification geodesic distance seed-growing method supervised classification framework support vector machine training nonseed superpixel classification public benchmark dataset

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interactive image segmentation based on the appearance model and orientation energy

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COMPUTER VISION AND image UNDERSTANDING 2022年第0期217卷 103371-103371页

作者： Qu, Shaojun Tan, Huang Li, Qiaoliang Peng, Zili Hunan Normal Univ 36 Lushan Rd Changsha 410081 Peoples R China Hunan Youmei Technol Dev Co Ltd Chuanggu Ind Pk568 Queyuan Rd Changsha Peoples R China

Tang et al. (2013) proposed a graph-based image segmentation model by minimizing the distance between the object and background appearance overlap models. This model is very effective for interactive image segmentation. However, it is prone to isolated nodes when the colors or other appearances characteristic of the object and background are very similar. To improve the performance of this algorithm and related algorithms, we add new spatial distance and contour orientation energy terms to the energy function. Accordingly, we modify the construction of the energy graph. We add terminal nodes S and T and add prior constraints. Finally, we use the pseudoflow algorithm proposed by Hochbaum to calculate the maximum flow of the new energy graph. A large number of experiments on the MSRA dataset, BSD dataset and GrabCut dataset show that the results of the proposed method are better than those of many recently proposed image segmentation methods. The code is available at https://***/powerhope/AMOE.

关键词： Graph cut Appearance model Orientation energy Pseudoflow interactive image segmentation Binary segmentation

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A Bayesian Network Model for Automatic and interactive image segmentation

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IEEE TRANSACTIONS ON image PROCESSING 2011年第9期20卷 2582-2593页

作者： Zhang, Lei Ji, Qiang UtopiaCompression Corp Los Angeles CA 90064 USA Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

We propose a new Bayesian network (BN) model for both automatic and interactive image segmentation. A multilayer BN is constructed from an oversegmentation to model the statistical dependencies among superpixel regions, edge segments, vertices, and their measurements. The BN also incorporates various local constraints to further restrain the relationships among these image entities. Given the BN model and various image measurements, belief propagation is performed to update the probability of each node. image segmentation is generated by the most probable explanation inference of the true states of both region and edge nodes from the updated BN. Besides the automatic image segmentation, the proposed model can also be used for interactive image segmentation. While existing interactive segmentation (IS) approaches often passively depend on the user to provide exact intervention, we propose a new active input selection approach to provide suggestions for the user's intervention. Such intervention can be conveniently incorporated into the BN model to perform actively IS. We evaluate the proposed model on both the Weizmann dataset and VOC2006 cow images. The results demonstrate that the BN model can be used for automatic segmentation, and more importantly, for actively IS. The experiments also show that the IS with active input selection can improve both the overall segmentation accuracy and efficiency over the IS with passive intervention.

关键词： Active labeling Bayesian network (BN) image segmentation interactive image segmentation

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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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A Clicking Strategy Inspired by Inter-Individual Variation for interactive image segmentation 6

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

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6th Artificial Intelligence and Cloud Computing Conference (AICCC) / 5th Asia Digital image Processing Conference

作者： Shuofeng, Zhao Chunzhi, Gu Jun, Yu Chao, Zhang Univ Fukui Dept Engn Fukui Japan Toyohashi Univ Technol Dept Comp Sci & Engn Toyohashi Aichi Japan Niigata Univ Inst Sci & Technol Niigata 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： interactive image segmentation Click strategies Human Visual Psychology Inter-Individual variation segmentation

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LOOSECUT: interactive image segmentation WITH LOOSELY BOUNDED BOXES 24

LOOSECUT: INTERACTIVE IMAGE SEGMENTATION WITH LOOSELY BOUNDE...

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24th IEEE International Conference on image Processing (ICIP)

作者： Yu, Hongkai Zhou, Youjie Qian, Hui Xian, Min Wang, Song Univ South Carolina Columbia SC 29208 USA Zhejiang Univ Hangzhou Zhejiang Peoples R China Utah State Univ Logan UT 84322 USA

ISBN: (纸本)9781509021758

One popular approach to interactively segment an object of interest from an image is to annotate a bounding box that covers the object, followed by a binary labeling. However, the existing algorithms for such interactive image segmentation prefer a bounding box that tightly encloses the object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the bounding box only loosely covers the object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including an additional energy term to encourage consistent labeling of similar appearance pixels and a global similarity constraint to better distinguish the foreground and background. This MRF model is then solved by an iterated max-flow algorithm. We evaluate LooseCut in three public image datasets, and show its better performance against several state-of-the-art methods when increasing the bounding-box size.

关键词： interactive image segmentation Graph cut Loosely bounded box

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Hands-free interactive image segmentation Using Eyegaze

Hands-free Interactive Image Segmentation Using Eyegaze

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Sadeghi, M. Tien, G. Hamarneh, G. Atkins, M. S. Simon Fraser Univ Med Image Anal Lab Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780819475114

This paper explores a novel approach to interactive user-guided image segmentation, using eyegaze information as an input. The method includes three steps: 1) eyegaze tracking for providing user input, such as setting object and background seed pixel selection;2) an optimization method for image labeling that is constrained or affected by user input;and 3) linking the two previous steps via a graphical user interface for displaying the images and other controls to the user and for providing real-time visual feedback of eyegaze and seed locations, thus enabling the interactive segmentation procedure. We developed a new graphical user interface supported by an eyegaze tracking monitor to capture the user's eyegaze movement and fixations (as opposed to traditional mouse moving and clicking). The user simply looks at different parts of the screen to select which image to segment, to perform foreground and background seed placement and to set optional segmentation parameters. There is an eyegaze-controlled "zoom" feature for difficult images containing objects with narrow parts, holes or weak boundaries. The image is then segmented using the random walker image segmentation method. We performed a pilot study with 7 subjects who segmented synthetic, natural and real medical images. Our results show that getting used the new interface takes about only 5 minutes. Compared with traditional mouse-based control, the new eyegaze approach provided a 18.6% speed improvement for more than 90% of images with high object-background contrast. However, for low contrast and more difficult images it took longer to place seeds using the eyegaze-based "zoom" to relax the required eyegaze accuracy of seed placement.

关键词： computer-aided diagnosis (CAD) feature extraction human computer interaction eyegaze tracking interactive image segmentation random walker

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Enhanced Spatial Awareness for Deep interactive image segmentation 5th

Enhanced Spatial Awareness for Deep Interactive Image Segmen...

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Li, Haochen Ni, Jinlong Li, Zhicheng Qian, Yuxiang Wang, Tao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Peoples R China

ISBN: (纸本)9783031189159;9783031189166

Existing deep interactive segmentation approaches can extract the desired object for the user based on simple click interaction. However, the first click provided by the user on the full image space domain is generally too local to capture the global target object, which causes them to rely on a large number of subsequent click corrections for satisfactory results. This paper explores how to strengthen the spatial awareness of user interaction especially after the first click input and increase the stability during the continuous iterative correction process. We first design an interactive cascaded localization strategy to determine the spatial range of the potential target, and then integrate this space-aware prior into a dual-stream network structure as a soft constraint for the segmentation. The above operation can increase the network's attention to the target of interest under very limited user interaction. A new training and inference strategy is also developed to completely adapt the benefit from the space-aware guidance. Furthermore, an object shape related loss is designed to better supervise the network based on user-provided prior guidance. Explicit subject, controllable correction and flexible interaction can help to significantly boost the interactive segmentation performance. The proposed method achieves state-of-the-art performance on several popular benchmarks.

关键词： interactive image segmentation Spatial awareness Multi task learning

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