Region Adjacency Graph Research Articles

Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique

An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation.

영역분할과 컬러 특징을 이용한 건물 인식기법

This paper proposes a building recognition algorithm using watershed image segmentation algorithm and integrated region matching (IRM). To recognize a building, a preprocessing algorithm which is using Gaussian filter to remove noise and using canny edge extraction algorithm to extract edges is applied to input building image. First, images are segmented by watershed algorithm. Next, a region adjacency graph (RAG) based on the information of segmented regions is created. And then similar and small regions are merged. Second, a color distribution feature of each region is extracted. Finally, similar building images are obtained and ranked. The building recognition algorithm was evaluated by experiment. It is verified that the result from the proposed method is superior to color histogram matching based results.

Boundary-constrained multi-scale segmentation method for remote sensing images

Image segmentation is the key step of Object-Based Image Analysis (OBIA) in remote sensing. This paper proposes a Boundary-Constrained Multi-Scale Segmentation (BCMS) method. Firstly, adjacent pixels are aggregated to generate initial segmentation according to the local best region growing strategy. Then, the Region Adjacency Graph (RAG) is built based on initial segmentation. Finally, the local mutual best region merging strategy is applied on RAG to produce multi-scale segmentation results. During the region merging process, a Step-Wise Scale Parameter (SWSP) strategy is proposed to produce boundary-constrained multi-scale segmentation results. Moreover, in order to improve the accuracy of object boundaries, the property of edge strength is introduced as a merging criterion. A set of high spatial resolution remote sensing images is used in the experiment, e.g., QuickBird, WorldView, and aerial image, to evaluate the effectiveness of the proposed method. The segmentation results of BCMS are compared with those of the commercial image analysis software eCognition. The experiment shows that BCMS can produce nested multi-scale segmentations with accurate and smooth boundaries, which proves the robustness of the proposed method.

A Spatially-Constrained Color–Texture Model for Hierarchical VHR Image Segmentation

This letter presents a novel spatially-constrained color–texture model for hierarchical segmentation of very high resolution images. The segmentation starts with an initial partition, where the image is partitioned into many homogeneous regions. Then, the regions are regarded as node sets of a region adjacency graph, in which the distances of each pair of adjacent regions are calculated combining color and textural features with spatial constraint. Finally, a stepwise optimized region merging process is applied to obtain hierarchical segmentation results. Experiments and comparisons by using different satellite images are carried out to demonstrate the encouraging performance as well as the high efficiency of the proposed method.

A combined topological and statistical approach for interactive segmentation of 3D images

This paper presents a new framework for an interactive segmentation of 3D images. The framework is based on a bimodal data structure defined by a region adjacency graph (RAG) that is associated with a hierarchical classification tree (HCT). The RAG provides information about the spatial and topological organisation of the extracted regions of the image. The HCT provides information about the similarities between the extracted regions of the image based on a predefined set of features. The first contribution of our work is the combination of a RAG and a HCT. An incremental system was obtained by defining operators that work with and on the RAG and the HCT. If a static predefined processing chain has been defined, these operators can be used in batch mode. If a scheduler is available, they can be used in an adaptive manner. Finally, if a user chooses the operator to be used after each step, the operators can be used interactively. The second contribution of this paper is the formal description of these operators. To give the user the ability to incrementally improve the segmentation, powerful visualisation of the segmentation state and interfaces have been proposed, an important advantage of the proposed framework. To validate the proposed framework, a user study has been conducted in a concrete case of texture segmentation. Our system obtains very satisfactory results even for complex volumetric textures, and helps real users by providing high quality segmentations. The system has been tested by specialists in sonography to segment 3D ultrasound images of the skin. Some examples of segmentation are presented to illustrate the benefit of the interactivity provided by our approach.

An Improved Object-Based Video Segmentation Method

This paper presents an improved video segmentation scheme, which consists of two stages: initial segmentation and motion estimation. In the initial segmentation, the watershed transformation followed by a region adjacency graph guided region merging process is used to partition the first video frame into spatial homogenous regions. Then the motion of changed region is estimated. Based on the highly efficient quadratic motion model, the motion estimation is undertaken using Gauss-Newton Levenberg-Marquardt method to minimize the least-square error function. Experimental results show the proposed scheme provides high performance in terms of segmentation accuracy and video compression ratio.

Probabilistic Graphlet Transfer for Photo Cropping

As one of the most basic photo manipulation processes, photo cropping is widely used in the printing, graphic design, and photography industries. In this paper, we introduce graphlets (i.e., small connected subgraphs) to represent a photo's aesthetic features, and propose a probabilistic model to transfer aesthetic features from the training photo onto the cropped photo. In particular, by segmenting each photo into a set of regions, we construct a region adjacency graph (RAG) to represent the global aesthetic feature of each photo. Graphlets are then extracted from the RAGs, and these graphlets capture the local aesthetic features of the photos. Finally, we cast photo cropping as a candidate-searching procedure on the basis of a probabilistic model, and infer the parameters of the cropped photos using Gibbs sampling. The proposed method is fully automatic. Subjective evaluations have shown that it is preferred over a number of existing approaches.

An integer linear program for substitution-tolerant subgraph isomorphism and its use for symbol spotting in technical drawings

This paper tackles the problem of substitution-tolerant subgraph isomorphism which is a specific class of error-tolerant isomorphism. This problem aims at finding a subgraph isomorphism of a pattern graph S in a target graph G. This isomorphism only considers label substitutions and forbids vertex and edge insertion in G. This kind of subgraph isomorphism is often needed in pattern recognition problems when graphs are attributed with real values and no exact matching can be found between attributes due to noise.Our proposal to solve the problem of substitution-tolerant subgraph isomorphism relies on its formulation in the Integer Linear Program (ILP) formalism. Using a general ILP solver, the approach is able to find, if one exists, a mapping of a pattern graph into a target graph such that the topology of the searched graph is kept and the editing operations between the labels have a minimal cost.This technique is evaluated on both a set of synthetic graphs and a problem of symbol detection in technical drawings. In the second case, document and symbol images are represented by vector-attributed Region Adjacency Graphs built from a segmentation process. Obtained results demonstrate the relevance of considering subgraph isomorphism as an optimization process.

Watershed image segmentation and cloud classification from multispectral MSG–SEVIRI imagery

In this work a technique for cloud detection and classification from MSG–SEVIRI (Meteosat Second Generation–Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using order-invariant watershed algorithms, which are applied to the corresponding gradient images, computed by a multi-dimensional morphological operator. To reduce the over-segmentation produced by the watershed method, a RAG (Region Adjacency Graph) based region merging technique is applied, using region dissimilarity functions. Once the objects present in the image have been segmented, they are classified using a multi-threshold method based on physical considerations that takes into account the statistical parameters inside each region.

A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging

Image segmentation is becoming increasingly important in areas such as object-oriented image classification in the field of remote-sensing image analysis. We present a new approach for the image segmentation of a high-resolution pan-sharpened satellite image based on modified seeded-region growing and region merging. First, we conduct some pre-processing prior to image segmentation to improve segmentation quality. The initial seeds are automatically selected using the proposed block-based seed-selection method. After automatic selection of significant seeds, initial segmentation is achieved by applying the modified seeded-region growing procedure. Finally, region merging, based on a region-adjacency graph, is carried out in post-processing to obtain the final segmentation result. Experimental results demonstrate that the proposed method shows better performance than other approaches, and has good potential for its application to the segmentation of high-resolution satellite imagery.

A Resegmentation Approach for Detecting Rectangular Objects in High-Resolution Imagery

Image segmentation covers techniques for splitting one image into its components as homogeneous regions. This letter presents a resegmentation approach applied to urban images. Resegmentation represents the set of adjustments from a previous segmentation in which the elements are small regions with a high degree of spectral similarity (a condition known as oversegmentation). The focus of this letter is the house roofs, which are assumed to have a rectangular shape. These regions are merged according to an objective function, which, in the technique presented here, maximizes the rectangularity. With oversegmentation, we create a graph known as a region adjacency graph (RAG) that relates border elements. The main contribution of this letter is a technique, which works with the RAG, to maximize the objective function in a relaxationlike approach that splits and merges oversegmented regions until they form a meaningful object. The results showed that the method was able to detect rectangles according to user-defined parameters, such as the maximum level of the graph depth and the minimum degree of rectangularity for objects of interest.

Automatic foreground extraction by background elimination based on multiscale segmentation

We propose an approach to automatically extracting foreground regions. This is a novel method for segmenting salient objects from still images by background elimination. To extract foreground regions, a new method of background elimination based on multiscale segmentation is proposed to detect candidate object regions. To this end, we use a trimap consisting of foreground, background, and undefined regions and a region adjacency graph. A graph-cut technique is finally used to extract exact foreground regions from the candidates. Experimental results have shown that the proposed method yields a better foreground extraction than Kim's method under various environments containing multiple objects and clutter backgrounds in natural images.

Semantic labelling of SAR images with conditional random fields on region adjacency graph

Scene segmentation and semantic labelling are important for analysing and understanding synthetic aperture radar (SAR) images. In this study, the authors propose an effective and efficient labelling method for SAR images with conditional random fields on a region adjacency graph (CRF-RAG). More precisely, for an SAR image, a region adjacency graph (RAG) representation is firstly built on an initially over-segmentation of the image. Subsequently, a conditional random field (CRF) model is established over the RAG instead of over pixels. To train and infer the CRF-RAG model, a fast max-margin training strategy and the graph cut optimisation method are finally employed. As the CRF model is based on RAG, the computation complexity of the model can be reduced significantly. Compared to the Markov random field (MRF) model on RAG, the proposed CRF-RAG model is more efficient to incorporate different measures of SAR images, such as scattering intensity, texture and image context, into a unified model. Experiments on the TerraSAR-X imagery achieve promising results with modest computation cost, which validates the generality and flexibility of the proposed method.

A Novel Texture-Preceded Segmentation Algorithm for High-Resolution Imagery

Image segmentation is crucial to object-oriented remote sensing imagery analysis. In this paper, a novel texture-preceded segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as a loose constraint for later segmentation. The algorithm is based on the graph models of region adjacency graph and nearest neighbor graph, which can achieve fast node merging, depending on the global optimum. Here, a combined distance, composed of texture, spectral, and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. Then, the combined distance is applied to graph models, and the final segmentation result can be obtained iteratively by fast merging. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogeneous texture segments with spectral variability easily but can also detect the real object boundaries well. The experiments on high-resolution imagery show that, in terms of the same number of segments, the proposed algorithm can improve segmentation accuracy by 10%-20% compared to the results obtained by pure spectral features with Definiens Developer software.

Mobile merchandise evaluation service using novel information retrieval and image recognition technology

Consumers’ purchasing behavior has obviously changed in recent years with developments in social economics. This change has been evident in the decreased ratio of planned purchases but not in the increase of planned (or spontaneous) purchases. This act of spontaneous or otherwise unplanned purchasing is called “impulse buying”. However, buying under these conditions costs more money always comes with negative responses, such as complaints and regret. Therefore, we propose and have designed a new merchandise recommendation system, the Mobile Merchandise Evaluation Service Platform (MMESP). This is a three-tier system composed of Real-time Merchandise Identifying System (RMIS), Real-time Merchandise Evaluation System (RMES), and Real-time Merchandise Recommendation System (RMRS). With this system, Mobile Users (MUs) take pictures of merchandise and send them to MMESP, RMIS integrates Region Adjacency Graph (RAG) and Self-Organizing Maps (SOM) to gather information on the merchandise through those photographs, and. RMES and RMRS provide Intelligence Agents (IAs) and Multiple Document Summarization (MDS) to summarize recommendations on merchandise for MUs, all in real time.

WATERSHED SEGMENTATION WITH AUTOMATIC ALTITUDE SELECTION AND REGION MERGING BASED ON THE MARKOV RANDOM FIELD MODEL

Watershed transformation has proven to be an important tool in image analysis. However, the resulting image of watershed transformation is inevitably over-segmented due to the presence of noise or local irregularities in the input image. In this paper, the use of contour altitude at the immersion stage is proposed. Block gradient information computed from the input gradient image is defined and used to obtain a critical altitude of watershed flooding. This altitude is then refined based on entropy estimated from the intermediate segmentation result. Thereafter, an optimal altitude and its corresponding segmentation result can be obtained. Although this process can favorably reduce the number of regions, the quality of segmentation still requires further improvement. Hence, a Markov Random Field (MRF) model defined on a region adjacency graph (RAG) is adopted. Because the MRF model can merge neighboring regions that are similar in local statistic properties, it thus alleviates the over-segmentation problem and improves the quality of image segmentation. In the experimental studies, the proposed method has been tested using several benchmark images. It achieves improved appearance and energy indices in comparison with the results obtained by conventional methods.

Image segmentation via mean shift and loopy belief propagation

This paper presents a novel approach that can quickly and effectively partition images based on fully exploiting the spatially coherent property. We propose an algorithm named iterative loopy belief propagation (iLBP) to integrate the homogenous regions and prove its convergence. The image is first segmented by mean shift (MS) algorithm to form over-segmented regions that preserve the desirable edges and spatially coherent parts. The segmented regions are then represented by region adjacent graph (RAG). Motivated by k-means algorithm, the iLBP algorithm is applied to perform the minimization of the cost function to integrate the over-segmented parts to get the final segmentation result. The image clustering based on the segmented regions instead of the image pixels reduces the number of basic image entities and enhances the image segmentation quality. Comparing the segmentation result with some existing algorithms, the proposed algorithm shows a better performance based on the evaluation criteria of entropy especially on complex scene images.

Attention-driven salient edge(s) and region(s) extraction with application to CBIR

Selective visual attention plays an important role for humans to understand an image by intuitively emphasizing some salient parts. Such mechanism can be well applied in localized content-based image retrieval, due to the fact that in the context of CBIR, the user is only interested in a portion of the image and the rest of the image is irrelevant. Being aware of this, in this paper, the selective visual attention model (SVAM) is incorporated in the CBIR task to estimate the user's retrieval concept. In contrast with existing learning based retrieval algorithms which need relevance feedback strategy to get user's high-level semantic information, the proposed method does not need any user's interaction to provide the training data. From this point of view, our method can be regarded as the purely bottom-up manner while learning based algorithms belong to the top-down manner. Specifically, an improved saliency map computing algorithm is employed first. Then, based on the saliency map, an efficient salient edges and regions detection method is introduced. Moreover, the concepts of salient edge histogram descriptors (SEHDs) and salient region adjacency graphs (SRAGs) are proposed, respectively, for images’ similarity comparison. Finally, an integrated strategy is adopted for content-based image retrieval. Experiments show that the proposed algorithm can characterize the human perception well and achieve satisfying retrieval performance.

Region-Level Motion-Based Foreground Segmentation Under a Bayesian Network

This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to save computation time and be robust to noise effects, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest, within which the spatial relationships are represented by a region adjacency graph. Next, we consider the motion of the foreground objects and, hence, utilize the temporal coherence property in the regions detected. Thus, the foreground segmentation problem is formulated as follows. Given two consecutive image frames and the segmentation result priorly obtained, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner by maximizing the conditional joint probability density function of these two elements. To represent the conditional joint probability density function in a compact form, a Bayesian network is adopted, which is derived to model the interdependency of these two elements. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.

Flow Direction Scalar Field and Fast Scanning Method Based Image Segmentation

A fast automatic image segmentation method is presented through the combination of gradient vector flow (GVF) field and seeded region growing (SRG).First,a flow di- rection scalar (FDS) field is derived from GVF.Then,a new SRG method called fast scanning method (FSM) is proposed. Finally,region adjacency graph (RAG) based region merging is used to get the final result.This method is very efficient that it is possible to fit real-time applications if a fast GVF computation method is adopted.Experiments demonstrate its effectiveness and robustness.