scholarly journals Efficient Retrieval of Music Recordings Using Graph-Based Index Structures

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 336-352
Author(s):  
Frank Zalkow ◽  
Julian Brandner ◽  
Meinard Müller
Keyword(s):  
Nearest Neighbor ◽  
Response Times ◽  
Negative Impact ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Search Problem ◽  
Index Structures ◽  
Music Retrieval ◽  
Retrieval Systems ◽  
Music Recordings

Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications.

scholarly journals A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

2021 ◽  
Vol 11 (20) ◽  
pp. 9581
Author(s):  
Wei Wang ◽  
Yi Zhang ◽  
Genyu Ge ◽  
Qin Jiang ◽  
Yang Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Spatial Indexing ◽  
Cloud Data ◽  
Neighbor Search ◽  
Nearest Neighbor Searching ◽  
Indexing Structure ◽  
Balanced Tree

The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, Octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on Octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than Octree and 3D R*-tree.

scholarly journals Mobile Location Indexing Based On Synthetic Moving Objects

2019 ◽  
Vol 9 (4) ◽  
pp. 2556
Author(s):  
Thu Thu Zan ◽  
Sabai Phyu
Keyword(s):  
Performance Evaluation ◽  
Location Privacy ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Data Access ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Mobile Objects ◽  
Neighbor Search ◽  
Privacy And Confidentiality

Today, the number of researches based on the data they move known as mobile objects indexing came out from the traditional static one. There are some indexing approaches to handle the complicated moving positions. One of the suitable ideas is pre-ordering these objects before building index structure. In this paper, a structure, a presorted-nearest index tree algorithm is proposed that allowed maintaining, updating, and range querying mobile objects within the desired period. Besides, it gives the advantage of an index structure to easy data access and fast query along with the retrieving nearest locations from a location point in the index structure. A synthetic mobile position dataset is also proposed for performance evaluation so that it is free from location privacy and confidentiality. The detail experimental results are discussed together with the performance evaluation of KDtree-based index structure. Both approaches are similarly efficient in range searching. However, the proposed approach is especially much more save time for the nearest neighbor search within a range than KD tree-based calculation.

Bregman Hyperplane Trees for Fast Approximate Nearest Neighbor Search

2012 ◽  
Vol 3 (4) ◽  
pp. 75-87
Author(s):  
Bilegsaikhan Naidan ◽  
Magnus Lie Hetland
Keyword(s):  
Query Processing ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Data Sets ◽  
Search Performance ◽  
Space Partitioning ◽  
Neighbor Search ◽  
Order Of Magnitude ◽  
Exact Nearest Neighbor

This article presents a new approximate index structure, the Bregman hyperplane tree, for indexing the Bregman divergence, aiming to decrease the number of distance computations required at query processing time, by sacrificing some accuracy in the result. The experimental results on various high-dimensional data sets demonstrate that the proposed index structure performs comparably to the state-of-the-art Bregman ball tree in terms of search performance and result quality. Moreover, this method results in a speedup of well over an order of magnitude for index construction. The authors also apply their space partitioning principle to the Bregman ball tree and obtain a new index structure for exact nearest neighbor search that is faster to build and a slightly slower at query processing than the original.

An Error Minimizing Partitioning Method for the Nearest Neighbor Search

2013 ◽  
Vol 321-324 ◽  
pp. 2165-2170
Author(s):  
Seung Hoon Lee ◽  
Jaek Wang Kim ◽  
Jae Dong Lee ◽  
Jee Hyong Lee
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Computational Cost ◽  
Nearest Neighbor Search ◽  
High Dimensional ◽  
Index Structures ◽  
Cost Index ◽  
Data Set ◽  
High Dimensional Space ◽  
Neighbor Search

The nearest neighbor search in high-dimensional space is an important operation in many applications, such as data mining and multimedia databases. Evaluating similarity in high-dimensional space requires high computational cost; index-structures are frequently used for reducing computational cost. Most of these index-structures are built by partitioning the data set. However, the partitioning approaches potentially have the problem of failing to find the nearest neighbor that is caused by partitions. In this paper, we propose the Error Minimizing Partitioning (EMP) method with a novel tree structure that minimizes the failures of finding the nearest neighbors. EMP divides the data into subsets with considering the distribution of data sets. For partitioning a data set, the proposed method finds the line that minimizes the summation of distance to data points. The method then finds the median of the data set. Finally, our proposed method determines the partitioning hyper-plane that passes the median and is perpendicular to the line. We also make a comparative study between existing methods and the proposed method to verify the effectiveness of our method.

scholarly journals Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Wei Jiang ◽  
Fangliang Wei ◽  
Guanyu Li ◽  
Mei Bai ◽  
Yongqiang Ren ◽  
...  
Keyword(s):  
Road Network ◽  
Moving Objects ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Urban Transportation ◽  
Road Networks ◽  
Nearest Neighbor Search ◽  
Index Structure ◽  
Widespread Application ◽  
Neighbor Search

With the widespread application of location-based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast-updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree-indexed searching method is proposed to quickly find the answers to user-defined queries on frequently updating road networks. First, a novel index structure, called the double tree-hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index-enhanced search algorithm is proposed to quickly find the k -nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state-of-the-art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.

scholarly journals Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion

2015 ◽  
pp. 307-313 ◽  
Author(s):  
Michael Hund ◽  
Michael Behrisch ◽  
Ines Färber ◽  
Michael Sedlmair ◽  
Tobias Schreck ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Search Problem ◽  
Problem Statement ◽  
Neighbor Search

scholarly journals K Nearest Neighbor Search in Navigation Systems

2005 ◽  
Vol 1 (3) ◽  
pp. 207-224 ◽  
Author(s):  
Maytham Safar
Keyword(s):  
Nearest Neighbor ◽  
Response Times ◽  
Shortest Paths ◽  
Network Connectivity ◽  
Nearest Neighbor Search ◽  
Navigation Systems ◽  
Large Network ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Spatial Network Databases

A frequent type of query in a car navigation system is to find theknearest neighbors (kNN) of a given query object (e.g., car) using the actual road network maps. With road networks (spatial networks), the distances between objects depend on their network connectivity and it is computationally expensive to compute the distances (e.g., shortest paths) between objects. In this paper, we propose a novel approach to efficiently and accurately evaluatekNN queries in a mobile information system that uses spatial network databases. The approach uses first order Voronoi diagram and Dijkstra's algorithm. This approach is based on partitioning a large network to small Voronoi regions, and then pre-computing distances across the regions. By performing across the network computation for only the border points of the neighboring regions, we avoid global pre-computation between every object-pair. Our empirical experiments with real-world data sets show that our proposed solution outperforms approaches that are based on on-line distance computation by up to one order of magnitude. In addition, our approach has better response times than approaches that are based on pre-computation.

scholarly journals Multi-PQTable for Approximate Nearest-Neighbor Search

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 190
Author(s):  
Xinpan Yuan ◽  
Qunfeng Liu ◽  
Jun Long ◽  
Lei Hu ◽  
Songlin Wang
Keyword(s):  
Image Retrieval ◽  
Nearest Neighbor ◽  
Computing Time ◽  
Nearest Neighbor Search ◽  
Image Feature ◽  
Index Structure ◽  
Product Quantization ◽  
Feature Vectors ◽  
Approximate Nearest Neighbor ◽  
Ann Search

Image retrieval or content-based image retrieval (CBIR) can be transformed into the calculation of the distance between image feature vectors. The closer the vectors are, the higher the image similarity will be. In the image retrieval system for large-scale dataset, the approximate nearest-neighbor (ANN) search can quickly obtain the top k images closest to the query image, which is the Top-k problem in the field of information retrieval. With the traditional ANN algorithms, such as KD-Tree, R-Tree, and M-Tree, when the dimension of the image feature vector increases, the computing time will increase exponentially due to the curse of dimensionality. In order to reduce the calculation time and improve the efficiency of image retrieval, we propose an ANN search algorithm based on the Product Quantization Table (PQTable). After quantizing and compressing the image feature vectors by the product quantization algorithm, we can construct the image index structure of the PQTable, which speeds up image retrieval. We also propose a multi-PQTable query strategy for ANN search. Besides, we generate several nearest-neighbor vectors for each sub-compressed vector of the query vector to reduce the failure rate and improve the recall in image retrieval. Through theoretical analysis and experimental verification, it is proved that the multi-PQTable query strategy and the generation of several nearest-neighbor vectors are greatly correct and efficient.

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