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For shapes represented as closed planar contours, we introduce a class of functionals which are invariant with respect to the Euclidean group and which are obtained by performing integral operations. While such integral invariants...
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For shapes represented as closed planar contours, we introduce a class of functionals which are invariant with respect to the Euclidean group and which are obtained by performing integral operations. While such integral invariants enjoy some of the desirable properties of their differential counterparts, such as locality of computation (which allows matching under occlusions) and uniqueness of representation (asymptotically), they do not exhibit the noise sensitivity associated with differential quantities and, therefore, do not require presmoothing of the input shape. Our formulation allows the analysis of shapes at multiple scales. Based on integral invariants, we define a notion of distance between shapes. The proposed distance measure can be computed efficiently and allows warping the shape boundaries onto each other; its computation results in optimal point correspondence as an intermediate step. Numerical results on shape matching demonstrate that this framework can match shapes despite the deformation of subparts, missing parts and noise. As a quantitative analysis, we report matching scores for shape retrieval from a database.
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As non-rigid 3D shape plays increasingly important roles in practical applications, this paper addresses its retrieval problem by considering three aspects: shape representation, retrieval optimization, and shape filtering. (1) Fo...
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As non-rigid 3D shape plays increasingly important roles in practical applications, this paper addresses its retrieval problem by considering three aspects: shape representation, retrieval optimization, and shape filtering. (1) For shape representation, two kinds of features are considered. We first propose a new integration kernel based local descriptor, and then an efficient voting scheme is designed for shape representation. Besides, we also study the commute times as shape distributions, which grasp the spatial shape information globally. Both of them capture shape information from different viewpoints based on the same embedding basis. (2) We then study the typical problem of retrieval optimization. Prior works show poor stability under different similarity windows. To deal with this deficiency, we propose to model the problem as a distance mapping on a graph in spectral manifold space. (3) Usually, for each retrieval input, a list is returned and there may be lots of irrelevant results. We develop an algorithm to filter them out by combining multiple kernels. Finally, three public datasets are employed for performance evaluation and the results show that the studied techniques have contributed a lot in promoting the recognition rate of non-rigid 3D shapes.
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We propose a new shape-baeed, query-by-example, image database retrieval method that is able to match a query image to one of the images in the database, based on a whole or partial match. The proposed method has two key component...
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We propose a new shape-baeed, query-by-example, image database retrieval method that is able to match a query image to one of the images in the database, based on a whole or partial match. The proposed method has two key components: the architecture of the retrieval and the features used. Both play a role in the overall retrieval efficacy. The proposed architecture is based on the analysis of connected components and holes in the query and database images. The features we propose to use are geometric in nature, and are invariant to translation, rotation and scale. Each of the suggested three features is not new per se, but combining them to produce a compact and efficient feature vector is. We use hand-sketched, rotated and scaled query images to test the proposed method using a database of 500 logo images. We compare the performance of the suggested features with the performance of the moment invariants (a set of commonly-used shape features). The suggested features match the moment invariants in rotated and scaled queries and consistently surpass them in hand-sketched queries. Moreover, results clearly show that the proposed architecture significantly increases the performance of the two feature sets.
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In this work, we propose a shape signature named Distance Interior Ratio (DIR) that utilizes intersection pattern of the distribution of line segments with the shape. To improve the efficiency of the histogram-based shape signatur...
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In this work, we propose a shape signature named Distance Interior Ratio (DIR) that utilizes intersection pattern of the distribution of line segments with the shape. To improve the efficiency of the histogram-based shape signature, we present a histogram alignment method for adjusting the interval of the histogram according to the distance distribution. The experimental result shows a 3.25% improvement using the proposed histogram alignment. When compared to other shape signatures, our experimental result gives a 77.69% retrieval rate using MPEG7 Part B dataset [Latecki, et al. (2000)[14]]. (C) 2016 Elsevier B.V. All rights reserved.
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As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this...
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As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245% better performance than related shape-matching methods during precision―recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).
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Similarity-based retrieval from databases of isolated visual shapes has become an important information retrieval problem. The goal of the current work is to achieve high retrieval speed with reasonable retrieval effectiveness, an...
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Similarity-based retrieval from databases of isolated visual shapes has become an important information retrieval problem. The goal of the current work is to achieve high retrieval speed with reasonable retrieval effectiveness, and support for partial and occluded shape queries. In the proposed method, histograms of local shape parts are coded as index vectors. TO increase retrieval accuracy, a rich set of parts at all scales of the shape is used; specifically, the parts are defined as connected sequences of regions in curvature scale space.
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This work proposes novel locally affine invariant descriptors for shape representation. The descriptors are theoretically simple and solid, derived from the matrix theories. They can be used for matching and retrieval of shapes un...
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This work proposes novel locally affine invariant descriptors for shape representation. The descriptors are theoretically simple and solid, derived from the matrix theories. They can be used for matching and retrieval of shapes under affine transformation, articulated motion or nonrigid deformation. Comparisons of the work with the state-of-the-art shape descriptors are performed based on synthetic and some well-known databases. The experiments validate that the proposed descriptors achieve higher retrieval accuracy and have faster running speed than most of other approaches.
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Improving query quality and robustness is a hot topic in information and image retrieval field, which has resulted in many interesting works. To address the same problem for deformable non-rigid 3D shape retrieval, two topics are ...
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Improving query quality and robustness is a hot topic in information and image retrieval field, which has resulted in many interesting works. To address the same problem for deformable non-rigid 3D shape retrieval, two topics are considered in this paper. The first one we discussed is shape representation, which is related to feature extraction and fusion. For feature extraction, we create a global feature to achieve a coarser-scale shape appearance description. Then, to alleviate the drawbacks of retrieval by single feature, we develop a novel fusion method for multiple feature fusion, which turns out to be superior to weighted sum approach with a low complexity. The second topic studied in this paper is to further refine the retrieval results by introducing a new retrieval guidance algorithm based on category prediction. To evaluate the proposed methods, experiments on three popular non-rigid datasets are carried out. The evaluation results suggest that our shape representation method has achieved state-of-the-art performance. Then, by adjusting the retrieval results of existing methods, our retrieval guidance algorithm has promoted the accuracy with nice effects.
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3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for te...
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3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods are compared.
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Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pair...
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Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape similarity measure. For a given similarity measure, a new similarity is learned through graph transduction. The new similarity is learned iteratively so that the neighbors of a given shape influence its final similarity to the query. The basic idea here is related to PageRank ranking, which forms a foundation of Google Web search. The presented experimental results demonstrate that the proposed approach yields significant improvements over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91.61 percent on the MPEG-7 data set, which is the highest ever reported in the literature. Moreover, the learned similarity by the proposed method also achieves promising improvements on both shape classification and shape clustering.
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