Attributed Relational Graph Research Articles

A Phishing Web Pages Detection Algorithm Based on Nested Structure of Earth Mover’s Distance (Nested-EMD)

Web Phishing has become a big threat to online applications such as financial services,it steals user identities and credentials by imitating the sites of service providers such as banks.This paper proposes a novel architecture of Phishing Web detection which gives the function modules and processing workflow,and a visual based Web page similarity detecting algorithm.Based on the image of the suspicious Web page,the algorithm first divides Web page into sub-block images from which features and relations are Abstracted and the ARG(Attributed Relational Graph)of the Web page is formed.Then based on the ARG of two Web pages,we get the Nested-EMD(Earth Mover's Distance)of the two pages as their similarity,and then the decision can be concluded by comparing the similarity degree between two Web pages.The algorithm is implemented and compared with the latest international researches,and it is shown that the algorithm is better in accuracy and robustness according to the experiment.

A Multimodal Approach for Face Modeling and Recognition

In this paper, we present a fully automated multi- modal (3-D and 2-D) face recognition system. For the 3-D modality, we model the facial image as a 3-D binary ridge image that contains the ridge lines on the face. We use the principal curvature to extract the locations of the ridge lines around the important facial regions on the range image (i.e., the eyes, the nose, and the mouth.) For matching, we utilize a fast variant of the iterative closest point to match the ridge image of a given probe image to the archived ridge images in the database. The main advantage of this approach is reducing the computational complexity by two orders of magnitude by relying on the ridge lines. For the 2-D modality, we model the face by an attributed relational graph (ARG), where each node of the graph corresponds to a facial feature point. At each facial feature point, a set of attributes is extracted by applying Gabor wavelets to the 2-D image and assigned to the node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of geometrical features that defines the mutual relations between the edges is extracted from the Delaunay triangles and stored in the ARG model. The similarity measure between the ARG models that represent the probe and gallery images is used for 2-D face recognition. Finally, we fuse the matching results of the 3-D and the 2-D modalities at the score level to improve the overall performance of the system. Different techniques for fusion, such as the Dempster-Shafer theory of evidence and weighted sum of scores are employed and tested using the facial images in the third experiment dataset of the Face Recognition Grand Challenge version 2.0.

제품 설계 정보 재사용을 위한 그래프 기반의 부품 영상 정보와 설계 정보의 병합

Recently, distributed collaborative development environment has been recognized an alternative environment for product development in which multidisciplinary participants are naturally involving. Reuse of Product design information has long been recognized as one of core requirements for efficient product development. This paper addresses an image-based retrieval system to support product design information reuse. In the system, product images obtained from multi-modal devices are utilized to reuse design information. The proposed system conducts the segmentation of a product image by using a labeling method and generates an attributed relational graph (ARG) that represents properties of segmented regions and their relationships. The generated ARG is extended by integrating corresponding part/assembly information. In this manner, the reuse of assembly design information using a product image has been realized. The main advantages of the presented system are following. First, the system is not dependent to specific design tools, because it utilizes multimedia images that can be obtained easily from peripheral devices. Second ratio-based features extracted from images enable image retrievals that contain various sizes of parts. Third, the system has shown outstanding search performance, because we applied various information of segmented part regions and their relationships between parts.

A multimedia support system for collaborative assembly design

In collaboration, multimedia information/data act as very important tools to share product design and engineering knowledge between project participants. However, current multimedia in collaboration provide only communication protocols and have limitations on capturing any engineering information imposed on product design. Hence, this paper aims to introduce a new framework of multimedia support for assembly design (AsD). The multimedia are linked to remote and distributed engineering information/data, which are core sources of AsD decision making. In doing so, rather than directly interfacing and sharing engineering information/data, the new framework will provide design participants with a seamless way of collaboration. Attributed relational graphs (ARGs) are adopted to employ an efficient representation of image contents. An extended ARG is proposed to incorporate information about assembly relationships and mathematical decision-making models with ARG-formatted graphical information. This research employs a service-oriented architecture (SOA) remotely and transparently to exchange multimedia, assembly relationships and mathematical model information for multimedia support. MSS-AD (multimedia support system for collaborative AsD), a prototype system, has been implemented to show the feasibility of the idea proposed in this paper.

MM-DSS: Integrating multimedia and decision-making knowledge in decision support systems

Abstract Multimedia (MM) information systems have been utilized for decision-making tasks since they were developed in the early 1990s. Few research studies to date have paid attention to the integrated mode of MM information with a decision support system (DSS) or have investigated the role of MM information and DSS from the perspective of model-based knowledge management. This paper proposes a multimedia decision support system (MM-DSS) to address the above unexplored issues. The proposed MM-DSS manages MM information with the use of a newly developed extended attributed relational graph (ARG) and the structured modeling (SM) technique in order to support complicated decision-making processes intelligently and systematically. This paper illustrates the development of the new extended ARG and the SM method, and their integration into mathematical decision-making knowledge. The proposed MM-DSS responds to the needs of representation, storage, and retrieval of MM information in decision-making processes. Results of simulation show that the proposed MM-DSS is successful in tackling an optimization problem of factory scheduling.

Face recognition using face-ARG matching

In this paper, we propose a novel line feature-based face recognition algorithm. A face is represented by the Face-ARG model, where all the geometric quantities and the structural information are encoded in an Attributed Relational Graph (ARG) structure, then the partial ARG matching is done for matching Face-ARG's. Experimental results demonstrate that the proposed algorithm is quite robust to various facial expression changes, varying illumination conditions and occlusion, even when a single sample per person is given.

Simultaneous tracking of multiple body parts of interacting persons

This paper presents a framework to simultaneously segment and track multiple body parts of interacting humans in the presence of mutual occlusion and shadow. The framework uses multiple free-form blobs and a coarse model of the human body. The color image sequence is processed at three levels: pixel level, blob level, and object level. A Gaussian mixture model is used at the pixel level to train and classify individual pixel based on color. Relaxation labeling in an attribute relational graph (ARG) is used at the blob level to merge the pixels into coherent blobs and to represent inter-blob relations. A twofold tracking scheme is used that consists of blob-to-blob matching in consecutive frames and blob-to-body-part association within a frame. The tracking scheme resembles multi-target, multi-association tracking (MMT). A coarse model of the human body is applied at the object level as empirical domain knowledge to resolve ambiguity due to occlusion and to recover from intermittent tracking failures. The result is ‘ARG–MMT’: ‘attribute relational graph based multi-target, multi-association tracker.’ The tracking results are demonstrated for various sequences including ‘punching,’ ‘hand-shaking,’ ‘pushing,’ and ‘hugging’ interactions between two people. This ARG–MMT system may be used as a segmentation and tracking unit for a recognition system for human interactions.

Region-based CBIR in GIS with local space filling curves to spatial representation

In this paper we present a region-based retrieval method for satellite images using motif co-occurrence matrix (MCM) in conjunction with spatial relationships. Each image is decomposed into coherent segments, MCM is computed for each region and the spatial relationship among them are evaluated by using a ∗-tree representation. The image is represented by an attributed relational graph (ARG) where nodes contain the visual feature (MCM) and edges represent spatial relationship. Principal component analysis show the usefulness of MCM as a feature.

Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms

A method for segmentation and recognition of image structures based on graph homomorphisms is presented in this paper. It is a model-based recognition method where the input image is over-segmented and the obtained regions are represented by an attributed relational graph (ARG). This graph is then matched against a model graph thus accomplishing the model-based recognition task. This type of problem calls for inexact graph matching through a homomorphism between the graphs since no bijective correspondence can be expected, because of the over-segmentation of the image with respect to the model. The search for the best homomorphism is carried out by optimizing an objective function based on similarities between object and relational attributes defined on the graphs. The following optimization procedures are compared and discussed: deterministic tree search, for which new algorithms are detailed, genetic algorithms and estimation of distribution algorithms. In order to assess the performance of these algorithms using real data, experimental results on supervised classification of facial features using face images from public databases are presented.

Recognition of partially occluded objects using probabilistic ARG (attributed relational graph)-based matching

In this paper, we propose a novel 2-D partial matching algorithm using the model-based probabilistic analysis of feature correspondences in the relation vector space, which is quite robust to shape variations due to noise and occlusions and invariant to 2-D geometric transformations as well. We represent an object using the attributed relational graph (ARG) model with nodes (features) of a set of the binary relation vectors. By defining relation vector space which can describe the structural information of an object centered at a specific feature, and modeling distortions due to partial occlusion or the input noise statistically in this space, lost features can be easily identified, so that the partial matching is performed efficiently. The proposed partial matching algorithm consists of two-phases. First, a finite number of candidate subgraphs are selected in an image, by using the logical constraint embedding local and structural consistency as well as the correspondence measure between model and image features. Second, the feature loss detection is done iteratively by the error detection and voting scheme through the error analysis in the relation vector space. Experimental results on real images demonstrate that the proposed algorithm has superior performance to those of the conventional relaxation algorithms, by localizing target objects robustly and correctly even in severely noisy and occluded scenes.

Structural pattern recognition using genetic algorithms with specialized operators

This paper presents a genetic algorithm (GA)-based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph (ARG) matching technique. The objective of our work is to improve the GA-based ARG matching procedures leading to a faster convergence rate and better quality mapping between a scene ARG and a set of given model ARGs. In this study, potential solutions are represented by integer strings indicating the mapping between scene and model vertices. The fitness of each solution string is computed by accumulating the similarity between the unary and binary attributes of the matched vertex pairs. We propose novel crossover and mutation operators, specifically for this problem. With these specialized genetic operators, the proposed algorithm converges to better quality solutions at a faster rate than the standard genetic algorithm (SGA). In addition, the proposed algorithm is also capable of recognizing multiple instances of any model object. An efficient pose-clustering algorithm is used to eliminate occasional wrong mappings and to determine the presence/pose of the model in the scene. We demonstrate the superior performance of our proposed algorithm using extensive experimental results.

Fast Retrieval by Spatial Structure in Image Databases

The state-of-the-art approach for speeding-up the time responses in databases is using Spatial Access Methods (SAMs) like e.g. R-trees. However, these methods do not treat image content directly (e.g. objects are approximated by their minimum bounding rectangles), nor can they handle images with multiple regions. The proposed approach extends the existing framework of indexing using SAMs to treat image content in conjunction with two well-known image-matching methods, namely the editing distance on Attributed Relational Graphs (ARGs) and the Hungarian method for graph matching. It provides index support for the two most common types of similarity queries, referred to as range and nearest-neighbor queries and has many desirable properties. For instance, it handles even complex queries specifying multiple objects (such as queries by image example), it returns exactly the same answers with the sequential scan methods (without indexing) and works with any SAM (e.g. R-tress) and with any image distance function provided that it satisfies the so-called Lower Bounding Principle .

Fast Retrieval by Spatial Structure in Image Databases

The state-of-the-art approach for speeding-up the time responses in databases is using Spatial Access Methods (SAMs) like e.g. R-trees. However, these methods do not treat image content directly (e.g. objects are approximated by their minimum bounding rectangles), nor can they handle images with multiple regions. The proposed approach extends the existing framework of indexing using SAMs to treat image content in conjunction with two well-known image-matching methods, namely the editing distance on Attributed Relational Graphs (ARGs) and the Hungarian method for graph matching. It provides index support for the two most common types of similarity queries, referred to as range andnearest-neighbor queries and has many desirable properties. For instance, it handles even complex queries specifying multiple objects (such as queries by image example), it returns exactly the same answers with the sequential scan methods (without indexing) and works with any SAM (e.g. R-tress) and with any image distance function provided that it satisfies the so-calledLower Bounding Principle .

ImageMap: an image indexing method based on spatial similarity

We introduce ImageMap, as a method for indexing and similarity searching in image databases (IDBs). ImageMap answers "queries by example" involving any number of objects or regions and taking into account their interrelationships. We adopt the most general image content representation, that is, Attributed Relational Graphs (ARGs), in conjunction with the well-accepted ARG editing distance on ARGs. We tested ImageMap on real and realistic medical images. Our method not only provides for visualization of the data set, clustering and data mining, but it also achieves up to 1,000-fold speed-up in search over sequential scanning, with zero or very few false dismissals.

Using relaxation technique for region-based object recognition

Abstract We address the problem of object recognition in computer vision. We represent each model and the scene in the form of attributed relational graph (ARG). A multiple region representation is provided at each node of the scene ARG to increase the representation reliability. The process of matching the scene ARG against the stored models is facilitated by a novel method for identifying the most probable representation from among the multiple candidates. The scene and model graph matching is accomplished using probabilistic relaxation which has been modified to minimise the label clutter. The experimental results obtained on real data demonstrate promising performance of the proposed recognition system.

Model-based stroke extraction and matching for handwritten Chinese character recognition

This paper proposes a model-based structural matching method for handwritten Chinese character recognition (HCCR). This method is able to obtain reliable stroke correspondence and enable structural interpretation. In the model base, the reference character of each category is described in an attributed relational graph (ARG). The input character is described with feature points and line segments. The strokes and inter-stroke relations of input character are not determined until being matched with a reference character. The structural matching is accomplished in two stages: candidate stroke extraction and consistent matching. All candidate input strokes to match the reference strokes are extracted by line following and then the consistent matching is achieved by heuristic search. Some structural post-processing operations are applied to improve the stroke correspondence. Recognition experiments were implemented on an image database collected in KAIST, and promising results have been achieved.

Recognition of handprinted Chinese characters by constrained graph matching

A model-based handwritten Chinese character recognition (HCCR) system is proposed. The characters are represented by attributed relational graphs (ARG) using strokes as ARG vertices. A number of vector relational attributes are also used in the representation to improve the performance of the translation and scale invariant and rotation sensitive recognition system. Since the ETL-8 database is very noisy and broken strokes are commonly encountered, a suitable homomorphic energy function is proposed that allows the segments of a broken stroke of a test character to be matched to the corresponding model stroke. The homomorphic ARG matching energy is minimised using the self-organising Hopfield neural networks [1] [Suganthan, P.N., Teoh, E.K., Mital, D.P., A self-organising Hopfield network for attributed relational graph matching, Image and Vision Computing, 13(1) (1995) 61–73]. An effective formulation is introduced to determine the matching score. The formulation does not penalise the matching scores of test characters with broken strokes. Experiments were performed with 100 classes of characters in the ETL-8 database and 98.9% recognition accuracy has been achieved.

On-Line Chinese Character Recognition by Incorporating Human Knowledge

A stroke order and number free method for on-line recognition of Chinese characters is proposed. Both input characters and the model characters are represented with complete attributed relational graphs (ARGs). The ARGs of the model base are built according to the human knowledge of the segment relation structure of Chinese characters. For the recognition purpose, an optimal matching measure between two ARGs is defined, and the graph matching is formulated as a search problem of finding the minimum cost path in a state space tree, using the A * algorithm. Moreover, to reduce the search time of the A *, besides a heuristic estimate, a novel strategy is employed which again uses the human knowledge of the segment position structure of Chinese characters to prune the tree. Our experimental results demonstrate the efficiency of the proposed method.

Online Chinese character recognition using attributed relational graph matching

A structural method for online recognition of Chinese characters is proposed which is stroke-order free and allows variations in stroke type and stroke number. Both input characters and the model characters are represented with complete attributed relational graphs (ARGs). An optimal matching measure between two ARGs is defined. Classification of an input character can be implemented by inexactly matching its ARG against every ARG of the model base. The matching procedure is formulated as a search problem of finding the minimum-cost path in a state space tree using the A* algorithm. To speed up the search of the A*, in addition to a heuristic estimate, a novel strategy that utilises the geometric position information of strokes of Chinese characters to prune the tree is employed. The efficiency of the method is demonstrated by the promising experimental results.

From 3D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations

We propose an algorithm allowing the construction of a structural representation of the cortical topography from a T1-weighted 3D MR image. This representation is an attributed relational graph (ARG) inferred from the 3D skeleton of the object made up of the union of gray matter and cerebro-spinal fluid enclosed in the brain hull. In order to increase the robustness of the skeletonization, topological and regularization constraints are included in the segmentation process using an original method: the homotopically deformable regions. This method is halfway between deformable contour and Markovian segmentation approaches. The 3D skeleton is segmented in simple surfaces (SSs) constituting the ARG nodes (mainly cortical folds). The ARG relations are of two types: first, theSS pairs connected in the skeleton; second, theSS pairs delimiting a gyrus. The described algorithm has been developed in the frame of a project aiming at the automatic detection and recognition of the main cortical sulci. Indeed, the ARG is a synthetic representation of all the information required by the sulcus identification. This project will contribute to the development of new methodologies for human brain functional mapping and neurosurgery operation planning.