Approximate similarity retrieval with M-trees

Zezula, Pavel; Savino, Pasquale; Amato, Giuseppe; Rabitti, Fausto

doi:10.1007/s007780050069

Cited by 79 publications

(63 citation statements)

References 13 publications

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“…This is also the case for the solution proposed by Clarkson [13], which applies to exact NN search over generic metric spaces, but whose space requirements depend on the error probability. Finally, Zezula et al [25] have recently proposed approximate NN search algorithms with good cost performance. However, since the effective error is not bounded by any function of the input parameters, their algorithms do not provide guarantees on the quality of the result.…”

Section: Discussionmentioning

confidence: 99%

PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces

Ciaccia

Patella

Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)

117

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Section: Discussionmentioning

confidence: 99%

PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces

Ciaccia

Patella

Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)

117

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“…M-Trees [21] use a hierarchical decomposition of the space. A technique that uses a proximity measure to decide which tree nodes can be pruned, even if their bounding regions overlap the query region, is proposed in [22].…”

Section: Background and Related Workmentioning

confidence: 99%

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Lazaridis

Αξενόπουλος

Rafailidis

et al. 2013

Signal Processing: Image Communication

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In this paper, a novel framework for multimodal search and retrieval of rich media objects is presented. The searchable items are media representations consisting of multiple modalities, such as 2D images, 3D objects and audio files, which share a common semantic concept. A manifold learning technique based on Laplacian Eigenmaps was appropriately modified in order to merge the low-level descriptors of each separate modality and create a new low-dimensional multimodal feature space, where all media objects can be mapped irrespective of their constituting modalities. To accelerate search and retrieval and make the framework suitable even for web-scale applications, a multimedia indexing scheme is adopted, which represents each object of the dataset by the ordering of a number of reference objects. Moreover, the hubness property is introduced in this paper as a criterion to select the most representative reference objects, thus, achieving the maximum possible performance of indexing. The content-based similarity of the multimodal descriptors is also used to automatically annotate the objects of the dataset using a predefined set of attributes. Annotation propagation is utilized to approximate the multimodal descriptors for multimodal queries that do not belong to the dataset.

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“…• Approximate methods, such as approximate search with the M-tree [9], MetricMap [10], kNN graphs [see, e.g., 4, pp. 637-641], SASH [11], the proximity preserving order of Chávez et al [12] or several others [13][14][15].…”

Section: Other Indexing Approachesmentioning

confidence: 99%

The Basic Principles of Metric Indexing

Hetland

2009

Studies in Computational Intelligence

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Summary. This chapter describes several methods of similarity search, based on metric indexing, in terms of their common, underlying principles. Several approaches to creating lower bounds using the metric axioms are discussed, such as pivoting and compact partitioning with metric ball regions and generalized hyperplanes. Finally, pointers are given for further exploration of the subject, including non-metric, approximate, and parallel methods.

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Approximate similarity retrieval with M-trees

Cited by 79 publications

References 13 publications

PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces

PAC nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

The Basic Principles of Metric Indexing

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