Exact evaluation of targeted stochastic watershed cuts

Pattern Analysis and …, 2001

International Journal of Computer Applications, 2013

Morphological image processing has been widely used for segmentation of binary, grayscale and color images. To extend the concept of segmentation, an ordering of the data is required. In this research paper, an effective methodology for digital color image segmentation has been publicized with stochastic gradients and watershed algorithm. The results demonstrate that combining of these two strategies has been very helpful for image segmentation and for computer vision, even in noisy images. The efficiency of the proposed methodology has been explained by experimental results and statistical measurements.

EURASIP Journal on Advances in Signal Processing, 2008

Marker-driven watershed segmentation attempts to extract seeds that indicate the presence of objects within an image. These markers are subsequently used to enforce regional minima within a topological surface used by the watershed algorithm. The classification-driven watershed segmentation (CDWS) algorithm improved the production of markers and topological surface by employing two machine-learned pixel classifiers. The probability maps produced by the two classifiers were utilized for creating markers, object boundaries, and the topological surface. This paper extends the CDWS algorithm by (i) enabling automated feature extraction via independent components analysis and (ii) improving the segmentation accuracy by introducing heterogeneous stacking. Heterogeneous stacking, an extension of stacked generalization for object delineation, improves pixel labeling and segmentation by training base classifiers on multiple target concepts extracted from the original ground truth, which are subsequently fused by the second set of classifiers. Experimental results demonstrate the effectiveness of the proposed system on real world images, and indicate significant improvement in segmentation quality over the base system.

14th International Conference on Hybrid Intelligent Systems (HIS 2014), 2014

This paper presents a multi-label automatic GrabCut technique for the problem of image segmentation. GrabCut is considered as one of the binary-label segmentation techniques because it is based on the famous s/t graph cut minimization technique for image segmentation. This paper extends the automatic binary-label GrabCut to a multi-label technique that can segment a given image into its natural segments without user intervention. Since multi-label segmentation is an NP-hard problem, the proposed algorithm converts the segmentation problem into multiple iterative piecewise binary label GrabCut segmentations. This implies separating one segment from the image, under consideration, per iteration. In this way, the proposed algorithm maintains the powerful advantage of the GrabCut to get the optimal solution for the segmentation problem. Evaluation of the segmentation results was carried out using different accuracy metrics from the literature. The evaluations were conducted with human ground truth segmentations from Berkeley benchmark dataset of natural images. Although human segmentations are semantically more meaningful, experiments showed that the proposed multi-label GrabCut provided matching segmentation results to that of individual humans with acceptable accuracy.

7th International Conference on Image Processing and its Applications, 1999

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009

This paper deals with unsupervised segmentation of hyperspectral images. It is based on the stochastic watershed, an approach to estimate a probability density function (pdf) of contours of an image using MonteCarlo simulations of watershed segmentations. In particular, it is introduced for the first time a multiscale framework for the computation of the pdf of contours using the stochastic watershed. Two multiscale approaches are considered: i) a linear scale-space using Gaussian filters, ii) a nonlinear morphological scale-space pyramid using levelings. In addition, a multiscale pyramid obtained by modifying the size of the random markers is also studied. Then, it is shown how the pdf of contours can finally be segmented using the non-parametric waterfalls algorithm. The performances of the proposed methods are compared using two examples of standard remote sensing hyperspectral images.

2006

Abstract Image segmentation using tree pruning (TP) and watershed (WS) has been presented in the framework of the image forest transform (IFT)-a method to reduce image processing problems related to connectivity into an optimum-path forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on different properties of the IFT which make TP more robust than WS for automatic segmentation tasks.

Lecture Notes in Computer Science

Due to its broad impact in many image analysis applications, the problem of image segmentation has been widely studied. However, there still does not exist any automatic segmentation procedure able to deal accurately with any kind of image. Thus semi-automatic segmentation methods may be seen as an appropriate alternative to solve the segmentation problem. Among these methods, the marker-based watershed has been successfully involved in various domains. In this algorithm, the user may locate the markers, which are used only as the initial starting positions of the regions to be segmented. We propose to base the segmentation process also on the contents of the markers through a supervised pixel classification, thus resulting in a knowledge-based watershed segmentation where the knowledge is built from the markers. Our contribution has been evaluated through some comparative tests with some state-of-the-art methods on the well-known Berkeley Segmentation Dataset.

IEEE Signal Processing Letters

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered-(a) How do the boundaries of the watershed operator behave? (b) Which loss function does the watershed operator optimize? (c) How does watershed operator relate with existing ideas from machine learning. In this article, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, MORPHMEDIAN, resulting in the maximum margin principle. It is then shown that watersheds form a particular class of MORPHMEDIAN classifiers. Using the ensemble technique, watersheds are also extended to ensemble watersheds. These techniques are compared with relevant methods from literature and it is shown that watersheds perform better than SVM on some datasets, and ensemble watersheds usually outperform random forest classifiers.

Le Centre pour la Communication Scientifique Directe - HAL - Inria, 2022

Watershed transform, formulated based on robust mathematical morphological operations, has been one of the most reliable image segmentation methods for years. However, it is constrained by the problem of over-segmentation. To encounter this limitation, region merging approaches such as waterfall and P-algorithm exist in the literature, which processes the entire image iteratively. In this paper, we introduce the concept of watershed arcs that acts as the connected partition lines between the segments on an image and explore several properties. We define the operation of removing an arc that results in merging two neighbor segments and apply it to generate hierarchical segmentation of an input image. At each level, a graph is constructed solely from the representatives of the watershed arcs in the previous level, and the set of arcs to be removed at that level is determined by applying a watershed transformation only on the graph. As the cardinality of the graph reduces drastically at each level, a hierarchy of partitions is produced in a comparatively short amount of time than that of the existing methods. The proposed method is applied to a Digital Elevation Model data to extract hierarchical river basins. We also evaluated the performance of this method in image segmentation compared with some state-of-the-art methods.