An Index Structure for Data Mining and Clustering

Wang, Xiong; Wang, Jason Tsong-Li; Lin, King-Ip; Shasha, Dennis; Shapiro, Bruce A.; Zhang, Kaizhong

doi:10.1007/s101150050009

Cited by 49 publications

(55 citation statements)

References 16 publications

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“…In approximate search techniques based on Vector Approximation (VA)-Files [18], dimensionality reduction is obtained by quantizing the original data objects. Other techniques that fall in the category of space transformation are FastMap [19], mainly used in vector spaces, and MetricMap [20] suitable for metric spaces.…”

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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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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“…A number of methods have been proposed for embedding arbitrary metric spaces into a Euclidean or pseudo-Euclidean space [7,10,13,20,23,26,28]. Some of these methods, in particular MDS [28], Bourgain embeddings [7,12], LLE [20] and Isomap [23] are not applicable for online similarity retrieval, because they still need to evaluate exact distances between the query and most or all database objects.…”

Section: Related Workmentioning

confidence: 99%

“…Some of these methods, in particular MDS [28], Bourgain embeddings [7,12], LLE [20] and Isomap [23] are not applicable for online similarity retrieval, because they still need to evaluate exact distances between the query and most or all database objects. Online queries can be handled by Lipschitz embeddings [12], FastMap [10], MetricMap [26] and SparseMap [13], which can readily compute the embedding of the query, measuring only a small number of exact distances in the process. These four methods are the most related to our approach.…”

Section: Related Workmentioning

confidence: 99%

“…We briefly go over Lipschitz embeddings [12], Bourgain embeddings [7,12], FastMap [10] and MetricMap [26]. All these methods, with the exception of Bourgain embeddings, can be used for efficient approximate nearest neighbor retrieval.…”

Section: Background On Embeddingsmentioning

confidence: 99%

“…MetricMap [26] is an extension of FastMap, that maps X into a a pseudo-Euclidean space. The experiments in [26] report that MetricMap tends to do better than FastMap when X is non-Euclidean.…”

Section: Fastmap and Metricmapmentioning

confidence: 99%

See 2 more Smart Citations

Learning Euclidean Embeddings for Indexing and Classification

Athitsos¹,

Alon²,

Sclaroff³

et al. 2004

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BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original featurebased distance measure.

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