Shape Retrieval Methods Research Articles

A comparison of methods for sketch-based 3D shape retrieval

Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval methods have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites [1,2].

A Survey on Partial Retrieval of 3D Shapes

Content-based shape retrieval techniques can facilitate 3D model resource reuse, 3D model modeling, object recognition, and 3D content classification. Recently more and more researchers have attempted to solve the problems of partial retrieval in the domain of computer graphics, vision, CAD, and multimedia. Unfortunately, in the literature, there is little comprehensive discussion on the state-of-the-art methods of partial shape retrieval. In this article we focus on reviewing the partial shape retrieval methods over the last decade, and help novices to grasp latest developments in this field. We first give the definition of partial retrieval and discuss its desirable capabilities. Secondly, we classify the existing methods on partial shape retrieval into three classes by several criteria, describe the main ideas and techniques for each class, and detailedly compare their advantages and limits. We also present several relevant 3D datasets and corresponding evaluation metrics, which are necessary for evaluating partial retrieval performance. Finally, we discuss possible research directions to address partial shape retrieval.

CM-BOF: visual similarity-based 3D shape retrieval using Clock Matching and Bag-of-Features

Content-based 3D object retrieval has become an active topic in many research communities. In this paper, we propose a novel visual similarity-based 3D shape retrieval method (CM-BOF) using Clock Matching and Bag-of-Features. Specifically, pose normalization is first applied to each object to generate its canonical pose, and then the normalized object is represented by a set of depth-buffer images captured on the vertices of a given geodesic sphere. Afterwards, each image is described as a word histogram obtained by the vector quantization of the image's salient local features. Finally, an efficient multi-view shape matching scheme (i.e., Clock Matching) is employed to measure the dissimilarity between two models. When applying the CM-BOF method in non-rigid 3D shape retrieval, multidimensional scaling (MDS) should be utilized before pose normalization to calculate the canonical form for each object. This paper also investigates several critical issues for the CM-BOF method, including the influence of the number of views, codebook, training data, and distance function. Experimental results on five commonly used benchmarks demonstrate that: (1) In contrast to the traditional Bag-of-Features, the time-consuming clustering is not necessary for the codebook construction of the CM-BOF approach; (2) Our methods are superior or comparable to the state of the art in applications of both rigid and non-rigid 3D shape retrieval.

A Contour Representation and Retrieval Algorithm based on Polar-histogram

This paper presents a novel method for contour-based shape retrieval. First, they come from contour points which can represent the shape very well as the object contour points got by contour tracing and denoising. Corner points, sampled points and two vector sets are obtained by leading vectors from each point to the contour centroid point. Then, the polar histogram of directions and distance in the vector sets are used to describe the shape. Finally, it combines the similarity measurement criterion of two contours with the polar histogram of the two feature points. The experiment result shows that our algorithm is efficient and is with better performance in the robustness to the scaling, rotation and translation, compared with the traditional algorithms.

A comparison of methods for non-rigid 3D shape retrieval

Non-rigid 3D shape retrieval has become an active and important research topic in content-based 3D object retrieval. The aim of this paper is to measure and compare the performance of state-of-the-art methods for non-rigid 3D shape retrieval. The paper develops a new benchmark consisting of 600 non-rigid 3D watertight meshes, which are equally classified into 30 categories, to carry out experiments for 11 different algorithms, whose retrieval accuracies are evaluated using six commonly utilized measures. Models and evaluation tools of the new benchmark are publicly available on our web site [1].

Shape retrieval based on manifold learning by fusion of dissimilarity measures

Content-based image retrieval (CBIR) is one of the most important research areas with applications in digital libraries, multimedia databases and the internet. Colour, texture, shape and spatial relations between objects are major features used in retrieval. Shape features are powerful clues for object identification. In this study, for improving retrieval accuracy, dissimilarities of contour and region-based shape retrieval methods were used. It is assumed that the fusion of two categories of shape description causes a considerable improvement in retrieval performance. The main goal in this study is to propose a new feature vector to coincide semantic and Euclidean distances. To accomplish this, the desired topological manifold was learnt by a distance-driven non-linear feature extraction method. The experiments showed that the geometrical distances between the samples on the manifold space are more related to their semantic distance. The proposed method was compared with other well-known approaches by MPEG-7 part B and Fish shape data sets. The results confirmed the effectiveness and validity of the proposed method.

A novel hybrid method in trademark image retrieval

This paper intends to propose an improved hybrid retrieval method in trademark image, and to build up a trademark image database to solve trademark image query problems. With the rapid increase of commercial activity in recent years, marks, logos and registered trademarks have been massively produced. Therefore, how to retrieve trademarks from a large database becomes a challenge. There are still some disadvantages of shape representations and retrieval methods. Some can well represent shapes, but are timeconsuming in normalization or need to classify images before retrieving. In this paper, a novel Fourier-Centroid-Histogram Descriptor (FCHD) is presented to overcome the drawbacks of existing shape representation techniques. The FCHD combines the benefits of the contour- based and region-based descriptors, and needs no process of classifying. In this study, the retrieval efficiency of this FCHD is compared with that of Fourier-Centroid descriptor (FCD), and shape distribution function (SD). Experimental results indicate that the proposed improved method can retrieve the trademark image more efficiently and rapidly.

3D Shape and Structure Retrieval Method of Mechanical Parts Based on Recursive Segmentation

To realize reusable design, the retrieval of existing engineering design cases is necessary in which the most difficult problem is shape similarity comparison. Therefore, a 3D shape and structure retrieval method of mechanical parts based on recursive segmentation is put forward. The initial position of mechanical parts is obtained by generating convex bounding box of the mechanical parts and the normalization transformation of mechanical parts is realized. The solid model is segmented by generating adaptive bounded segmentation plane. The ordered full binary tree is built through recursive algorithm with the change detection of both the geometry and topology of the model. The feature vectors combined with engineer features are constructed to obtain the similarity comparison of the solid model in non-root nodes by means of similarity comparison of the vectors. The breath-first and variable weight traversal of the ordered full binary tree is realized. The similarity of 3D shape and structure between the mechanical parts is obtained via the similarity comparison of the solid model in non-root nodes. The application and verification of 3D shape and structure retrieval method of mechanical parts based on recursive segmentation in prototype system indicates that the method has higher calculation efficiency and judgment precision and it can reflect not only the global sketchy shape but also the local detail structure of the mechanical parts. It has important significance to realizing design reuse and improving design efficiency.

Multi-Fourier spectra descriptor and augmentation with spectral clustering for 3D shape retrieval

We propose a new method of similarity search for 3D shape models, given an arbitrary 3D shape as a query. The method features the high search performance enabled in part by our unique feature vector called Multi-Fourier Spectra Descriptor (MFSD), and in part by augmenting the feature vector with spectral clustering. The MFSD is composed of four independent Fourier spectra with periphery enhancement. It allows us to faithfully capture the inherent characteristics of an arbitrary 3D shape object regardless of the dimension, orientation, and original location of the object when it is first defined. Given a 3D shape database, the augmentation with spectral clustering is done first by computing the p-minimum spanning tree of the whole data set, where p is a number usually much less than m, the size of the whole 3D shape data set. We then define the affinity matrix, which is a square matrix of size m by m, where each element of the matrix denotes the distance between two shape objects. The distance is computed in advance by traversing the p-minimum spanning tree. The eigenvalue decomposition is then applied to the affinity matrix to reduce dimensionality of the matrix, followed by grouping into k clusters. The cluster information is kept for augmenting the search performance when a query is given. With a series of benchmark data sets, we will demonstrate that our approach outperforms previously known methods for 3D shape retrieval.

Robust symbolic representation for shape recognition and retrieval

A new method for shape recognition and retrieval is proposed here. The suggested algorithm is based on several steps. The algorithm analyzes the contour of pairs of shapes. Their contours are recov...

A survey of content based 3D shape retrieval methods

Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domain-specific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve similar 3D objects. For visualization, 3D shapes are often represented as a surface, in particular polygonal meshes, for example in VRML format. Often these models contain holes, intersecting polygons, are not manifold, and do not enclose a volume unambiguously. On the contrary, 3D volume models, such as solid models produced by CAD systems, or voxels models, enclose a volume properly. This paper surveys the literature on methods for content based 3D retrieval, taking into account the applicability to surface models as well as to volume models. The methods are evaluated with respect to several requirements of content based 3D shape retrieval, such as: (1) shape representation requirements, (2) properties of dissimilarity measures, (3) efficiency, (4) discrimination abilities, (5) ability to perform partial matching, (6) robustness, and (7) necessity of pose normalization. Finally, the advantages and limitations of the several approaches in content based 3D shape retrieval are discussed.

Shape retrieval using triangle-area representation and dynamic space warping

In this paper, we present a shape retrieval method using triangle-area representation for nonrigid shapes with closed contours. The representation utilizes the areas of the triangles formed by the boundary points to measure the convexity/concavity of each point at different scales (or triangle side lengths). This representation is effective in capturing both local and global characteristics of a shape, invariant to translation, rotation, and scaling, and robust against noise and moderate amounts of occlusion. In the matching stage, a dynamic space warping (DSW) algorithm is employed to search efficiently for the optimal (least cost) correspondence between the points of two shapes. Then, a distance is derived based on the optimal correspondence. The performance of our method is demonstrated using four standard tests on two well-known shape databases. The results show the superiority of our method over other recent methods in the literature.

Shape-based retrieval of video objects

The increasing availability of object-based video content requires new technologies for automatically extracting and matching of the low level features of arbitrarily shaped video. This paper proposes methods for shape retrieval of arbitrarily shaped video objects. Our methods take into account not only the still shape features but also the shape deformations that may occur in an object's lifespan. We compute the shape similarity of video objects by comparing the similarity of their representative temporal instances. We also describe motion of a video object via describing the deformations in an object's shape. Experimental results show that our proposed methods offer very good retrieval performance and match closely with the human ranking.

3D object comparison based on shape descriptors

Due to the recent improvements of 3D digitalisation, the number of 3D models available on the web is growing more and more, and there is an increasing demand for tools supporting the automatic search for 3D objects in digital archives. Existing methods for 3D shape retrieval roughly filter shape information and are mainly based on reasoning at the geometric level. In this paper, we discuss the advantages of approaching the shape matching problem through 3D graph descriptors, which describe the shape as a configuration of relevant subparts. In our approach, shapes are compared using a graph matching technique able to identify the most similar object portions. In particular, we investigate the properties of these descriptors and of our matching method in the CAD context.

Evaluation of a hypothesizer for silhouette-based 3-D object recognition

Shape retrieval and shape-based object recognition are closely related problems; however, they have different task contexts, performance criteria, and database characteristics. In previous work, we proposed a method for similarity-based 2-D shape retrieval using scale-space part decompositions, part-frequency distributions, and structural indexing. In this paper, we evaluate the use of that shape retrieval method as the hypothesis generation component of silhouette-based 3-D object recognition systems, using a performance criterion and test database appropriate for the new application.

Ellipse-based shape description and retrieval method

Using a group of ellipses to approach the shape contour, a new shape retrieval method is presented in this paper. In order to keep shape-based retrieval invariant to its position, orientation and size, the shape normalization method is presented. From our research, any closed shape contour can be uniquely decomposed into a group of ellipses, and the original shape contour can be re-constructed using the decomposed ellipses. The ellipse-based shape description and similar retrieval method is introduced in this paper. Based on ellipse’s contribution to shape contour, the decomposed ellipses are parted into low-order ellipses and high-order ellipses. The low-order ellipses measure the macroscopic feature of a shape contour, and the high-order ellipses measure the microscopic feature. The two-phase shape matching method is given. Through the experiment test, our method has better shape retrieval effect.

Deformed shape retrieval based on Markov model

A new deformed shape retrieval method based on the Markov model (MM) is proposed. The proposed method is translation, rotation and scale invariant. In addition, it is robust to various non-rigid deformations such as perspective, shear, occlusion, pincushion, barrel and other nonlinear distortions. The proposed method has been applied to two databases: geometric and character. The effectiveness and practicability of the proposed approach have been proven by various experimental results.