Nearest neighbor search on multimedia indexing structures

2009

As the volume of multimedia data available on internet is tremendously increasing, the content-based similarity search becomes a popular approach to multimedia retrieval. The most popular retrieval concept is the k nearest neighbor (kNN) search. For a long time, the kNN queries provided an effective retrieval in multimedia databases. However, as today's multimedia databases available on the web grow to massive volumes, the classic kNN query quickly loses its descriptive power. In this paper, we introduce a new similarity query type, the k distinct nearest neighbors (kDNN), which aims to generalize the classic kNN query to be more robust with respect to the database size. In addition to retrieving just objects similar to the query example, the kDNN further ensures the objects within the result have to be distinct enough, i.e. excluding near duplicates.

Information Systems, 2012

An efficient and universal similarity search solution is a holy grail for multimedia information retrieval. Most similarity indexes work by mapping the original multimedia objects into simpler representations, which are then searched by proximity using a suitable distance function.

Digital Media Processing for Multimedia Interactive Services - Proceedings of the 4th European Workshop on Image Analysis for Multimedia Interactive Services, 2003

Near neighbor searching in image databases is a multidimensional problem. The kd-tree is one of the first methods proposed for indexing multidimensional data. We describe optimizations of this method, and determine when they are appropriate. We discuss adaptations of the tree to feature extraction and indexing problems in multimedia data. Results show increased functionality and speed using the kd-tree as the index structure on a multimedia database.

Near neighbor searching in image databases is a multidimensional problem. The kd-tree is one of the first methods proposed for indexing multidimensional data. We describe optimizations of this method, and determine when they are appropriate. We discuss adaptations of the tree to feature extraction and indexing problems in multimedia data. Results show increased functionality and speed using the kd-tree as the index structure on a multimedia database.

… Conference on Storage and Retrieval for …, 1999

This paper describes a snapshot of work in progress on the development of an e cient le-access method for similarity searching in high-dimensional vector spaces. This method has applications in, for example, image databases where images are accessed ...

1998

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor search which corresponds to a computation of the voronoi cell of each data point. In a second step, we store the voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precomputation of the solution space, it is dynamic, i.e. it supports insertions of new data points. An extensive experimental evaluation of our technique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in the X-tree (up to a factor of 4).

Data & Knowledge Engineering, 2010

Similarity search is a very active area of research because of its usefulness in a set of modern applications, such as content-based image retrieval (CBIR), time series, spatial databases, data mining and multimedia databases in general. The usual way to do a similarity search is to map the objects to feature vectors and to model the search as a nearest neighbor query in the multidimensional space where vectors reside. The main critical issues to this process are: the distance function used to measure the proximity between vectors and the index method to accelerate the search. In this paper we propose a formal framework to perform similarity search that provides the user with a high degree of freedom in the choice of both the distance and the index structure used to organize the feature space. More specifically, we introduce a function to approximate eventually any distance function that can be used in conjunction with index structures that divide the feature space in multidimensional rectangular regions. Cases of use and experimental work are presented to demonstrate the applicability and the overhead of the framework.

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore pre-compute the result of any nearest-neighbor search which corresponds to a computation of the voronoi cell of each data point. In the second step, we store the voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precipitation of the solution space, it is dynamic, i.e. it supports insertions of new data points. An extensive experimental evaluation of our tech-unique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in the X-tree.

In this paper, we propose an efficient indexing method for high-dimensional multimedia databases using the filtering approach (known also as vector approximation approach). It is based on partitioning the data space in compact and disjoined regions. Then, each region will be approximated by two bit-strings according to the RA-Blocks technique. Our method improves the division strategy of regions compared to this one.

2004