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A novel local shape descriptor, geodesic connected graph, is proposed for the representation of three-dimensional (3D) prismatic CAD model represented by triangle meshes. First, the model is segmented using the extended Gaussian image. Second, the segmented areas which are visual from certain direction are connected by geodesic lines. Finally, a geodesic connected graph of feature areas is constructed...
Though elastic bunch graph matching (EBGM) has a good performance on face recognition in the distortion of facial expression, it is still not robust enough to in-depth rotation. To solve this problem, a novel face representation approach based on the space-filling tree is proposed in this paper. This kind of representation shows a better performance than Elastic bunch graph matching (EBGM) in in-depth...
Automatic transcription of historical documents is vital for the creation of digital libraries. In this paper we propose graph similarity features as a novel descriptor for handwriting recognition in historical documents based on Hidden Markov Models. Using a structural graph-based representation of text images, a sequence of graph similarity features is extracted by means of dissimilarity embedding...
Spectral graph theory is widely used in Pattern Recognition and Image Processing. It has found a wide range of applications ranging from clustering, to dimension reduction, or image representation. Spectral analysis for image registration raises however some challenges are seldom addressed in the literature. The difficulty stems from the fact that the eigenspectrum and eigenspace of the Laplacian...
A vision based exploration algorithm that invokes semantic cues for constructing a hybrid map of images - a combination of semantic and topological maps is presented in this paper. At the top level the map is a graph of semantic constructs. Each node in the graph is a semantic construct or label such as a room or a corridor, the edge represented by a transition region such as a doorway that links...
We describe an image representation that combines the representational power of graphs with the efficiency of the bag-of-words model. For each image in a data set, first, a graph is constructed from local patches of interest regions and their spatial arrangements. Then, each graph is represented with a histogram of sub graphs selected using a frequent subgraph mining algorithm in the whole data. Using...
In this paper, we propose a new computational model for visual saliency derived from the information maximization principle. The model is inspired by a few well acknowledged biological facts. To compute the saliency spots of an image, the model first extracts a number of sub-band feature maps using learned sparse codes. It adopts a fully-connected graph representation for each feature map, and runs...
In many image processing applications, and in the human visual system, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. A natural way to describe complex relationships, without loss of information, is to use hypergraphs. In this paper, we use a Color Image Neighborhood Hypergraph representation...
Extracting structure information from decorative character images is a challenging problem in the field of character recognition. The structure information of a decorative character image can be represented by a graph. However, the topologies of graphs are different even if they are the ones of the same character, because of various decorations. In this paper, we propose a method to extract a representative...
The spectrum of a graph has been widely used to characterize the properties of a graph and extract information from its structure. In this paper, we investigate the performance of Laplacian spectrum and multidimensional scaling (MDS) as shape recognition and clustering. Firstly, we extract boundary points to characterize the shape and to construct the Laplacian matrix. Secondly, the structural information...
We study in this paper the problem of using multiple-instance semi-supervised learning to solve image relevance feedback problem. Many multiple-instance learning algorithms have been proposed to tackle this problem; most of them only have a global representation of images. In this paper, we present a semi-supervised version of multiple instance learning. By taking into account both the multiple-instance...
In this paper, we adopt constrained relaxation for distributed multi-view video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate inter-view correlated side information without knowing the camera parameters. Moreover, graph-based representations of multi-view images are incorporated to form more distinctive feature constraints. The sparse data as a...
We formulate the problem of salient region detection in images as Markov random walks performed on images represented as graphs. While the global properties of the image are extracted from the random walk on a complete graph, the local properties are extracted from a k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a compact object. The equilibrium...
Support vector machines (SVM) has been widely applied in the area of content-based image retrieval in order to learn high-level concepts from low-level image features. Most existing SVM based image retrieval algorithms only rely on global-based features to represent the image content, which obviously can not well reflect the image semantic content. Region-based representations are far more close to...
The skeleton is a low-dimension shape representation of 3D objects useful for different areas, such as machine vision, image processing, computer graphics, character animation, and etc. The most common techniques for skeleton computing are based on the Reeb graph and the shortest path finding. Using only the shortest path algorithms for extracting the critical points and constructing the Reeb graph...
Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and to be matched may be very large, or even redundantly represent the shape information present. Since selective attention is a basic mechanism of the visual system, we explore whether there is a subset of salient points that...
In this paper, we propose a framework to model video sequences using spatiotemporal description of video shots. Spatiotemporal volumes are extracted thanks to an efficient segmentation algorithm. Video shots are described by building an adjacency graph which models the visual properties of the volumes and the spatiotemporal relationships between them. The cost of extracting visual descriptors for...
Sparse approximations that are evaluated using over complete learned dictionaries are useful in many image processing applications such as compression, denoising and feature extraction. Incorporating shift invariance into sparse representation of images can improve sparsity while providing a good approximation. The K-SVD algorithm adapts the dictionary based on a set of training examples, without...
Recent researches show that the benefits of image segmentation have been exploited in object categorization and recognition approaches. In most of these works, objects are segmented from the background around to increase recognition accuracy. However, it is generally hard to find a segmentation that captures all correct object boundaries in images of real world scene. So some researches begin to choose...
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