3D model retrieval using hybrid features and class information

Li, Bo; Johan, Henry

doi:10.1007/s11042-011-0873-3

Cited by 73 publications

(31 citation statements)

References 23 publications

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“…Recently, a hybrid descriptor named ZFDR comprising both geo metric and view information has been proposed in [54]. Li et al [55] combined the topological feature multiresolutional Reeb graph (MRG) based features and modified BOF based view fea tures.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

“…A survey of three typical semantics processing techniques (relevance feedback, machine learning, and ontology) is presented in [71]. Typical semantics based 3D retrieval approaches include relevance feed back [72], semantic labeling [73], neural networks [74], supervised [75 78] or semi supervised [79 81] learning, boosting [82], proto types [83], autotagging [84], spectral clustering [85], manifold ranking [86], semantic tree [87], feature dimension reduction [88], semantic subspaces [89], class distances [54], semantics annotation of 3D models [90], semantic correspondences [91], and sparse structure regularized ranking [92].…”

Section: Semantics Based 3d Model Retrieval Techniquesmentioning

confidence: 99%

“…They are Precision Recall (PR) diagram, Nearest Neighbor (NN), First Tier (FT), Second Tier (ST), E Measures (E), Discounted Cumu lated Gain (DCG) [7], and Average Precision (AP) [54]. We have developed code [1,2] to compute all of these metrics.…”

Section: Properties Of the Lsb Benchmarkmentioning

confidence: 99%

“…Please refer to the original paper [54 ) for more details about the feature extraction and retrieval process. [ 131,113).…”

Section: Hybrid Shape Descriptor Zfdr By LI And]ohan {54]mentioning

confidence: 99%

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A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

et al. 2015

Computer Vision and Image Understanding

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118

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a b s t r a c tLarge scale 3D shape retrieval has become an important research direction in content based 3D shape retrieval. To promote this research area, two Shape Retrieval Contest (SHREC) tracks on large scale com prehensive and sketch based 3D model retrieval have been organized by us in 2014. Both tracks were based on a unified large scale benchmark that supports multimodal queries (3D models and sketches). This benchmark contains 13680 sketches and 8987 3D models, divided into 171 distinct classes. It was compiled to be a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domain specific model types. Twelve and six distinct 3D shape retrieval methods have competed with each other in these two contests, respectively. To measure and compare the performance of the participating and other promising Query by Model or Query by Sketch 3D shape retrieval methods and to solicit state of the art approaches, we perform a more comprehensive comparison of twenty six (eighteen originally participating algorithms and eight additional state of the art or new) retrieval methods by evaluating them on the common benchmark. The benchmark, results, and evaluation tools are publicly available at our websites

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Section: Hybrid Techniquesmentioning

confidence: 99%

Section: Semantics Based 3d Model Retrieval Techniquesmentioning

confidence: 99%

Section: Properties Of the Lsb Benchmarkmentioning

confidence: 99%

“…Please refer to the original paper [54 ) for more details about the feature extraction and retrieval process. [ 131,113).…”

Section: Hybrid Shape Descriptor Zfdr By LI And]ohan {54]mentioning

confidence: 99%

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A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

et al. 2015

Computer Vision and Image Understanding

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118

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“…Although the advantage of feature fusion is obvious, how to combine multiple features in a generalized way is still challenging. In 3D field, Light Field Descriptor (LFD) is one of the classical feature fusion works [17], which integrated both Zernike moments and Fourier descriptors as features to describe shapes. For non-rigid shape retrieval, a direct sum approach was used to fuse SD-GDM and meshSIFT features in SHREC11 Contest [20], which results in a good result.…”

Section: Feature Fusionmentioning

confidence: 99%

Exploration in improving retrieval quality and robustness for deformable non-rigid 3D shapes

Kuang¹,

Jiang

et al. 2014

Multimed Tools Appl

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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 nonrigid 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 Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)

Pan

Chen

Kuo

2017

Computer Vision – ACCV 2016 Workshops

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Abstract. A novel solution for the content-based 3D shape retrieval problem using an unsupervised clustering approach, which does not need any label information of 3D shapes, is presented in this work. The proposed shape retrieval system consists of two modules in cascade: the irrelevance filtering (IF) module and the similarity ranking (SR) module. The IF module attempts to cluster gallery shapes that are similar to each other by examining global and local features simultaneously. However, shapes that are close in the local feature space can be distant in the global feature space, and vice versa. To resolve this issue, we propose a joint cost function that strikes a balance between two distances. Irrelevant samples that are close in the local feature space but distant in the global feature space can be removed in this stage. The remaining gallery samples are ranked in the SR module using the local feature. The superior performance of the proposed IF/SR method is demonstrated by extensive experiments conducted on the popular SHREC12 dataset.

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3D model retrieval using hybrid features and class information

Cited by 73 publications

References 23 publications

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries

Exploration in improving retrieval quality and robustness for deformable non-rigid 3D shapes

3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)

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