Interactive image segmentation by matching attributed relational graphs

Noma, Alexandre; Graciano, Ana Beatriz Vicentim; César, Roberto M.; Consularo, Luís Augusto; Bloch, Isabelle

doi:10.1016/j.patcog.2011.08.017

Cited by 50 publications

(34 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This approach has been used to solve many Computer Vision problems such as interactive natural image segmentation [6], computerassisted colorization [7] and point matching for non-rigid registration [8], among others [9].…”

Section: Fig 2 Data Flow Representing the Implemented Methodsmentioning

confidence: 99%

Motion Estimation from RGB-D Images Using Graph Homomorphism

Pires

César

Velho

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Abstract. We present an approach for motion estimation from videos captured by depth-sensing cameras. Our method uses the technique of graph matching to find groups of pixels that move to the same direction in subsequent frames. In order to choose the best matching for each patch, we minimize a cost function that accounts for distances on RGB and XYZ spaces. Our application runs at real-time rates for low resolution images and has shown to be a convenient framework to deal with input data generated by the new depth-sensing devices. The results show clearly the advantage obtained in the use of RGB-D images over RGB images.

show abstract

Section: Fig 2 Data Flow Representing the Implemented Methodsmentioning

confidence: 99%

Motion Estimation from RGB-D Images Using Graph Homomorphism

Pires

César

Velho

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

show abstract

“…It generates unsatisfactory results when foreground and background have close color distribution. Both maximal similarity-based region merging (MSRM) [22] and mating attributed relational graph (MARG) [23] begin with superpixels (the input image is divided into small homogenous regions). MSRM iteratively merges a region into a neighboring region which has the most similar color histogram and updates the histogram of newly merged region until there is no region to be merged.…”

Section: Related Workmentioning

confidence: 99%

“…Popular approaches include graph-cut based methods [10][11][12][13][14], edge based methods [15][16][17], random walk based methods [18][19][20], and region based methods [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Robust interactive image segmentation via graph-based manifold ranking

2015

Comp. Visual Media

View full text Add to dashboard Cite

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 2015-08-11; accepted: 2015-09-15 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.

show abstract

“…This method uses SPIHT algorithm, to separate the coefficients. Here an interactive segmentation approach is used to separate ROI (Noma et al, 2012) and not the BG of the image and the two regions are encoded separately using CSPIHT with high bitrate and low bitrate respectively. Finally the two regions are merged to reconstruct the image.…”

Section: Implicit Roi Codingmentioning

confidence: 99%

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

Sridevi¹

2013

Journal of Computer Science

View full text Add to dashboard Cite

A hybrid image compression techniques has been developed to compress medical images. Due to the extensive use of medical images like CT and MR scan, these medical imagery are stored for a longer period for the continuous monitoring of the patients and the amount of data associated with images is large and it occupies enormous storage capacity. So, the medical images need to be compressed to reduce the storage cost and for transmission without any loss. In this study, a hybrid method which combines the Listless Set Partitioning in Hierarchical Trees (LSPIHT) and the Contextual Vector Quantization (CVQ) method for the compression of brain images. Here, the region containing the most important information for diagnosis is called Region of Interest (ROI) and this is to be compressed with out any loss in the quality. In this method, the ROI is encoded separately using LSPIHT and the Back Ground region (BG) is encoded using CVQ. Finally, the two regions are merged together to get the reconstructed image. Our results show that the proposed method gives very good image quality for diagnosis without any degradable loss. The performance of the compression technique is evaluated using the parameters (CR, MSE, PSNR) and achieved better result compared to other existing methods. As a result, we strongly believe that using our method, we can overcome the limitations in storage and transmission of medical images that are produced day by day.

show abstract

Interactive image segmentation by matching attributed relational graphs

Cited by 50 publications

References 20 publications

Motion Estimation from RGB-D Images Using Graph Homomorphism

Motion Estimation from RGB-D Images Using Graph Homomorphism

Robust interactive image segmentation via graph-based manifold ranking

Medical Image Compression Technique Using Listless Set Partitioning in Hierarchical Trees and Contextual Vector Quantization for Brain Images

Contact Info

Product

Resources

About