Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances

Fu, Ada Wai-Chee; Chan, Polly Mei-shuen; Cheung, Yuen-Ting; Moon, Yiu Sang

doi:10.1007/pl00010672

Cited by 123 publications

(72 citation statements)

References 27 publications

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“…The ground distance function used by EMD must be a metric. Our algorithm satisfies the first property because we use the normalized Effective Area as the weight, as shown in (6). It is also clear that DistðÞ, shown in (7), is a metric because it only makes use of ratio combination of metric functions.…”

Section: Indexing Schemementioning

confidence: 96%

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Deformable Model Retrieval Based on Topological and Geometric Signatures

Tam

Lau

2007

IEEE Trans. Visual. Comput. Graphics

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-With the increasing popularity of 3D applications such as computer games, a lot of 3D geometry models are being created.To encourage sharing and reuse, techniques that support matching and retrieval of these models are emerging. However, only a few of them can handle deformable models, that is, models of different poses, and these methods are generally very slow. In this paper, we present a novel method for efficient matching and retrieval of 3D deformable models. Our research idea stresses using both topological and geometric features at the same time. First, we propose Topological Point Ring (TPR) analysis to locate reliable topological points and rings. Second, we capture both local and global geometric information to characterize each of these topological features. To compare the similarity of two models, we adapt the Earth Mover Distance (EMD) as the distance function and construct an indexing tree to accelerate the retrieval process. We demonstrate the performance of the new method, both in terms of accuracy and speed, through a large number of experiments.

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Section: Indexing Schemementioning

confidence: 96%

“…We use a VP-tree as it is such a method and has been reported to have good performance over others [6]. A VP-tree stores all the indexing data points in a tree.…”

Section: Indexing Schemementioning

confidence: 99%

Deformable Model Retrieval Based on Topological and Geometric Signatures

Tam

Lau

2007

IEEE Trans. Visual. Comput. Graphics

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“…Based on the method in which multimedia data contents is extracted, the literature has recognized several content-based retrieval techniques [14][15][16]35,37]. The traditional centralized content-based retrieval approaches are based on feature representation of multimedia data that can be categorized as three classes:…”

Section: Content-based Retrievalmentioning

confidence: 99%

“…The earlier models in this class include Quadtree [14], K-D-tree [15], and VP-tree [15]. The recent research has focused on models based on clusters [16,17].…”

Section: Content-based Retrievalmentioning

confidence: 99%

“…The recently proposed dimension reduction methods include locality preserving projection (LPP) [38] and heavy frequency vector (HFV) [17]. The partitionbased approaches normally employ very complex computations on features [15], which make these models inefficient for real-time multimedia applications. Moreover, the partition-based models do not consider feature space of different dimensions -i.e., multimedia objects from heterogeneous data sources [2].…”

Section: Content-based Retrievalmentioning

confidence: 99%

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Similarity‐Based Clustering Strategy for Mobile Ad Hoc Multimedia Databases

Yang

Hurson

2005

Mobile Information Systems

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Abstract. Multimedia data are becoming popular in wireless ad hoc environments. However, the traditional content-based retrieval techniques are inefficient in ad hoc networks due to the multiple limitations such as node mobility, computation capability, memory space, network bandwidth, and data heterogeneity. To provide an efficient platform for multimedia retrieval, we propose to cluster ad hoc multimedia databases based on their semantic contents, and construct a virtual hierarchical indexing infrastructure overlaid on the mobile databases. This content-aware clustering scheme uses a semantic-aware framework as the theoretical foundation for data organization. Several novel techniques are presented to facilitate the representation and manipulation of multimedia data in ad hoc networks: 1) using concise distribution expressions to represent the semantic similarity of multimedia data, 2) constructing clusters based on the semantic relationships between multimedia entities, 3) reducing the cost of content-based multimedia retrieval through the restriction of semantic distances, and 4) employing a selfadaptive mechanism that dynamically adjusts to the content and topology changes of the ad hoc networks. The proposed scheme is scalable, fault-tolerant, and efficient in performing content-based multimedia retrieval as demonstrated in our combination of theoretical analysis and extensive experimental studies.

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A Multi-metric Index for Euclidean and Periodic Matching

Vlachos¹,

Vagena

Castelli³

et al. 2005

Knowledge Discovery in Databases: PKDD 2005

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Abstract. In many classification and data-mining applications the user does not know a priori which distance measure is the most appropriate for the task at hand without examining the produced results. Also, in several cases, different distance functions can provide diverse but equally intuitive results (according to the specific focus of each measure). In order to address the above issues, we elaborate on the construction of a hybrid index structure that supports query-by-example on shape and structural distance measures, therefore lending enhanced exploratory power to the system user. The shape distance measure that the index supports is the ubiquitous Euclidean distance, while the structural distance measure that we utilize is based on important periodic features extracted from a sequence. This new measure is phase-invariant and can provide flexible sequence characterizations, loosely resembling the Dynamic Time Warping, requiring only a fraction of the computational cost of the latter. Exploiting the relationship between the Euclidean and periodic measure, the new hybrid index allows for powerful query processing, enabling the efficient answering of kNN queries on both measures in a single index scan. We envision that our system can provide a basis for fast tracking of correlated time-delayed events, with applications in data visualization, financial market analysis, machine monitoring/diagnostics and gene expression data analysis.

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Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances

Cited by 123 publications

References 27 publications

Deformable Model Retrieval Based on Topological and Geometric Signatures

Deformable Model Retrieval Based on Topological and Geometric Signatures

Similarity‐Based Clustering Strategy for Mobile Ad Hoc Multimedia Databases

A Multi-metric Index for Euclidean and Periodic Matching

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