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Journal articles on the topic 'Search for the nearest neighbour'

Author: Grafiati

Published: 25 May 2024

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1

Myasnikov, E. "Exact Nearest Neighbour Search within Constrained Neighbourhood Using the Forest of Vp-Tree-Like Structures." Journal of Physics: Conference Series 2096, no. 1 (November 1, 2021): 012199. http://dx.doi.org/10.1088/1742-6596/2096/1/012199.

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Abstract In this paper, we address the problem of fast nearest neighbour search. Unfortunately, well-known indexing data structures, such as vp-trees perform poorly on some datasets and do not provide significant acceleration compared to the brute force approach. In the paper, we consider an alternative solution, which can be applied if we are not interested in some fraction of distant nearest neighbours. This solution is based on building the forest of vp-tree-like structures and guarantees the exact nearest neighbour search in the epsilon-neighbourhood of the query point.

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2

N, MINOJINI, GAYATHRI R. KRISHNA, REKHA A, and SOWMIYAA P. "Dynamic Nearest Neighbour Search With Keywords." IJARCCE 4, no. 3 (March 30, 2015): 580–82. http://dx.doi.org/10.17148/ijarcce.2015.43139.

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3

Suhaibaha, A., A. A. Rahman, U. Uznir, F. Anton, and D. Mioc. "IMPROVING NEAREST NEIGHBOUR SEARCH IN 3D SPATIAL ACCESS METHOD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (October 26, 2016): 69–73. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-69-2016.

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Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.

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Hooda, Meenakshi, and Sumeet Gill. "Nearest Neighbour Search in k-dSLst Tree." Advances in Science, Technology and Engineering Systems Journal 5, no. 4 (July 2020): 160–66. http://dx.doi.org/10.25046/aj050419.

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5

Biswas, Sumana, Sreenatha G. Anavatti, and Matthew A. Garratt. "A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems." Robotics 8, no. 2 (April 26, 2019): 35. http://dx.doi.org/10.3390/robotics8020035.

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Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems. In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic environments. Both frameworks are capable of dynamically controlling a number of autonomous agents to accomplish multiple tasks at different locations. Furthermore, these two models are capable of dealing with dynamically changing environments. In both approaches, the Particle Swarm Optimization-based method is applied for path planning. The path planning approach combined with the obstacle avoidance strategy is integrated with the task assignment problem. In one framework, the Compromise View model is used for completing the tasks and a combination of clustering method with the Nearest-Neighbour Search model is used to assign tasks to the other framework. The frameworks are compared in terms of computational time and the resulting path length. Results indicate that the Nearest-Neighbour Search model is much faster than the Compromise View model. However, the Nearest-Neighbour Search model generates longer paths to accomplish the mission. By following the Nearest-Neighbour Search approach, agents can successfully accomplish their mission, even under uncertainties such as malfunction of individual agents. The Nearest-Neighbour Search framework is highly effective due to its reactive structure. As per requirements, to save time, after completing its own tasks, one agent can complete the remaining tasks of other agents. The simulation results show that the Nearest-Neighbour Search model is an effective and robust way of solving co-operative path planning combined with task assignment problems.

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6

Zhao, Ning, Jingyue Xu, and Gang Zhou. "Fault Diagnosis of Centrifugal Fan Based on Grid Search Optimized Voting Weighted KNN." Journal of Physics: Conference Series 2636, no. 1 (November 1, 2023): 012046. http://dx.doi.org/10.1088/1742-6596/2636/1/012046.

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Abstract Fan plays the roles of the forced draft fan, induced draft fan, primary air fan, seal fan, and powder exhauster. As an important auxiliary of the fossil-fuel power station, its working environment is harsh. Timely and efficient fault diagnosis can effectively reduce equipment failure and shutdown losses, and improve the efficiency of thermal power generation. K-Nearest Neighbour (KNN) has good classification ability for non-stationary data samples. In response to the shortcomings of the traditional KNN algorithm, this paper constructs a fault diagnosis model based on the voting weighted k-nearest neighbor algorithm. The model constructs a weight voting equation that is negatively correlated with the distance value based on the first k-nearest neighbors and then conducts fault diagnosis based on the voting score. We use grid search to optimize the model and select the k value in the model, and the relationship between the k value and accuracy was verified. The grid search optimization voting weighted k-nearest neighbor is used to diagnose the faults of nine common operating states of centrifugal fans, and the diagnostic accuracy can reach 100%.

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7

Suhaibah, A., U. Uznir, F. Anton, D. Mioc, and A. A. Rahman. "3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 87–93. http://dx.doi.org/10.5194/isprs-archives-xli-b2-87-2016.

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Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

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8

Suhaibah, A., U. Uznir, F. Anton, D. Mioc, and A. A. Rahman. "3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 87–93. http://dx.doi.org/10.5194/isprsarchives-xli-b2-87-2016.

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Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

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9

Cheung, King Lum, and Ada Wai-Chee Fu. "Enhanced nearest neighbour search on the R-tree." ACM SIGMOD Record 27, no. 3 (September 1998): 16–21. http://dx.doi.org/10.1145/290593.290596.

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10

Ali, Mohammed Eunus, Saif-ul-Islam Khan, Sharowar Md Shahriar Khan, and Md Nasim. "Spatio-temporal keyword search for nearest neighbour queries." Journal of Location Based Services 9, no. 2 (April 3, 2015): 113–37. http://dx.doi.org/10.1080/17489725.2015.1066887.

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11

Wang, Xing, Ji Chen, and Jiangxu Yu. "Optimised quantisation method for approximate nearest neighbour search." Electronics Letters 53, no. 3 (February 2017): 156–58. http://dx.doi.org/10.1049/el.2016.2810.

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12

Wang, Lijin, Yiwen Zhong, and Yilong Yin. "Nearest neighbour cuckoo search algorithm with probabilistic mutation." Applied Soft Computing 49 (December 2016): 498–509. http://dx.doi.org/10.1016/j.asoc.2016.08.021.

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13

Varpa, Kirsi, Kati Iltanen, and Martti Juhola. "Genetic Algorithm Based Approach in Attribute Weighting for a Medical Data Set." Journal of Computational Medicine 2014 (September 3, 2014): 1–11. http://dx.doi.org/10.1155/2014/526801.

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Genetic algorithms have been utilized in many complex optimization and simulation tasks because of their powerful search method. In this research we studied whether the classification performance of the attribute weighted methods based on the nearest neighbour search can be improved when using the genetic algorithm in the evolution of attribute weighting. The attribute weights in the starting population were based on the weights set by the application area experts and machine learning methods instead of random weight setting. The genetic algorithm improved the total classification accuracy and the median true positive rate of the attribute weighted k-nearest neighbour method using neighbour’s class-based attribute weighting. With other methods, the changes after genetic algorithm were moderate.

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14

Lifshits, Yury. "Nearest neighbor search." SIGSPATIAL Special 2, no. 2 (July 2010): 12–15. http://dx.doi.org/10.1145/1862413.1862417.

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15

Chatterjee, Bapi, Ivan Walulya, and Philippas Tsigas. "Concurrent linearizable nearest neighbour search in LockFree-kD-tree." Theoretical Computer Science 886 (September 2021): 27–48. http://dx.doi.org/10.1016/j.tcs.2021.06.041.

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16

Kam, Jeffery, and Scott Dick. "Comparing nearest-neighbour search strategies in the SMOTE algorithm." Canadian Journal of Electrical and Computer Engineering 31, no. 4 (2006): 203–10. http://dx.doi.org/10.1109/cjece.2006.259180.

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17

Wang, Liantao, Xuelei Hu, Bo Yuan, and Jianfeng Lu. "Active learning via query synthesis and nearest neighbour search." Neurocomputing 147 (January 2015): 426–34. http://dx.doi.org/10.1016/j.neucom.2014.06.042.

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18

Ahmed, Kazi Wasif, Md Momin Al Aziz, Md Nazmus Sadat, Dima Alhadidi, and Noman Mohammed. "Nearest neighbour search over encrypted data using intel SGX." Journal of Information Security and Applications 54 (October 2020): 102579. http://dx.doi.org/10.1016/j.jisa.2020.102579.

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19

Masudin, Ilyas, Risma F. Sa’diyah, Dana M. Utama, Dian Palupi Restuputri, and Ferry Jie. "Capacitated Vehicle Routing Problems: Nearest Neighbour vs. Tabu Search." International Journal of Computer Theory and Engineering 11, no. 4 (2019): 76–79. http://dx.doi.org/10.7763/ijcte.2019.v11.1246.

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20

Baek, SeongJoon, and Koeng-Mo Sung. "Fast K-nearest-neighbour search algorithm for nonparametric classification." Electronics Letters 36, no. 21 (2000): 1821. http://dx.doi.org/10.1049/el:20001249.

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21

Fatah, Haerul, and Agus Subekti. "PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS." Jurnal Pilar Nusa Mandiri 14, no. 2 (September 1, 2018): 137. http://dx.doi.org/10.33480/pilar.v14i2.894.

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Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

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22

Komorowski, Michał, and Tomasz Trzciński. "Random Binary Search Trees for approximate nearest neighbour search in binary spaces." Applied Soft Computing 79 (June 2019): 87–93. http://dx.doi.org/10.1016/j.asoc.2019.03.031.

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23

Prayoga, Vito Charlanda, and Novi Mardiana. "IMPLEMENTASI NEAREST NEIGHBOUR DAN TABU SEARCH DALAM OPTIMASI RUTE PENDISTRIBUSIAN PRODUK." Prosiding Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik 5 (December 9, 2023): 212. http://dx.doi.org/10.32897/sobat.2023.5.0.3099.

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One of the crucial activities undertaken by companies to meet customer demands is distribution. Distribution involves several challenges, one of which is determining optimal vehicle routes. This research aims to determine effective and efficient product distribution routes. The Nearest Neighbour and Tabu Search methods are employed to address the distribution route problem. The research results indicate that using the Nearest Neighbour method saves 17.7 km, 388.75 minutes, and Rp 40,430.77 compared to the company's initial routes. Meanwhile, the Tabu Search method yields savings of 21.25 km, 412.1 minutes, and Rp 43,525.64 when compared to the initial company routes.

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24

Gómez-Ballester, Eva, Luisa Micó, and Jose Oncina. "Some approaches to improve tree-based nearest neighbour search algorithms." Pattern Recognition 39, no. 2 (February 2006): 171–79. http://dx.doi.org/10.1016/j.patcog.2005.06.007.

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25

Tellez, Eric Sadit, Edgar Chavez, and Gonzalo Navarro. "Succinct nearest neighbor search." Information Systems 38, no. 7 (October 2013): 1019–30. http://dx.doi.org/10.1016/j.is.2012.06.005.

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26

G, Vasanthi. "Nearest Neighbors Search Algorithm for High Dimensional Data." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (July 30, 2020): 1215–18. http://dx.doi.org/10.5373/jardcs/v12sp8/20202636.

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27

Doshi, Ishita, Dhritiman Das, Ashish Bhutani, Rajeev Kumar, Rushi Bhatt, and Niranjan Balasubramanian. "LANNS." Proceedings of the VLDB Endowment 15, no. 4 (December 2021): 850–58. http://dx.doi.org/10.14778/3503585.3503594.

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Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks (HNSW), is unable to scale to large datasets of 100M records in high dimensions. In this paper, we propose LANNS, an end-to-end platform for Approximate Nearest Neighbor Search, which scales for web-scale datasets. Library for Large Scale Approximate Nearest Neighbor Search (LANNS) is deployed in multiple production systems for identifying top-K (100 ≤ k ≤ 200) approximate nearest neighbors with a latency of a few milliseconds per query, high throughput of ~2.5k Queries Per Second (QPS) on a single node, on large (e.g., ~ 180M data points) high dimensional (50-2048 dimensional) datasets.

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28

Onyezewe, Anozie, Armand F. Kana, Fatimah B. Abdullahi, and Aminu O. Abdulsalami. "An Enhanced Adaptive k-Nearest Neighbor Classifier Using Simulated Annealing." International Journal of Intelligent Systems and Applications 13, no. 1 (February 8, 2021): 34–44. http://dx.doi.org/10.5815/ijisa.2021.01.03.

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The k-Nearest Neighbor classifier is a non-complex and widely applied data classification algorithm which does well in real-world applications. The overall classification accuracy of the k-Nearest Neighbor algorithm largely depends on the choice of the number of nearest neighbors(k). The use of a constant k value does not always yield the best solutions especially for real-world datasets with an irregular class and density distribution of data points as it totally ignores the class and density distribution of a test point’s k-environment or neighborhood. A resolution to this problem is to dynamically choose k for each test instance to be classified. However, given a large dataset, it becomes very tasking to maximize the k-Nearest Neighbor performance by tuning k. This work proposes the use of Simulated Annealing, a metaheuristic search algorithm, to select optimal k, thus eliminating the prospect of an exhaustive search for optimal k. The results obtained in four different classification tasks demonstrate a significant improvement in the computational efficiency against the k-Nearest Neighbor methods that perform exhaustive search for k, as accurate nearest neighbors are returned faster for k-Nearest Neighbor classification, thus reducing the computation time.

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29

Lee, K. C. K., Baihua Zheng, and Wang-Chien Lee. "Ranked Reverse Nearest Neighbor Search." IEEE Transactions on Knowledge and Data Engineering 20, no. 7 (July 2008): 894–910. http://dx.doi.org/10.1109/tkde.2008.36.

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30

Papadopoulos, Stavros, Lixing Wang, Yin Yang, Dimitris Papadias, and Panagiotis Karras. "Authenticated Multistep Nearest Neighbor Search." IEEE Transactions on Knowledge and Data Engineering 23, no. 5 (May 2011): 641–54. http://dx.doi.org/10.1109/tkde.2010.157.

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31

Chung, Yu-Chi, I.-Fang Su, Chiang Lee, and Pei-Chi Liu. "Multiple k nearest neighbor search." World Wide Web 20, no. 2 (May 13, 2016): 371–98. http://dx.doi.org/10.1007/s11280-016-0392-2.

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32

Ashesh, K., and Dr G. Appa Rao. "Distributed Mining of Outliers from Large Multi-Dimensional Databases." International Journal of Engineering & Technology 7, no. 4.7 (September 27, 2018): 292. http://dx.doi.org/10.14419/ijet.v7i4.7.20564.

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A data point is given dataset is considered to be outlier when it is not distant to all its nearest neighbours. Obviously it is based on distance measure. However, in distributed environments it is challenging to detect outliers. Many approaches to mine outliers such environments came into existence. However, a faster and more efficient way is desired. In this paper we employ a novel index tree which is hierarchical in nature. Its hierarchical structure paves way for space pruning while its clustering property helps in faster search of finding neighbours of a given data point. Its time complexity is linear to the size of dataset and its dimensions. On top of the hierarchical tree (Hi-tree) nearest neighbour search avoids unnecessary computations besides pruning unpromising points. An algorithm by name Distributed Mining of Outliers using Hi-tree (DMOH) is proposed. The index tree can be exploited with parallel processing phenomenon. We built a prototype application to demonstrate proof of the concept. Our empirical study revealed the efficiency of the proposed algorithm on top of Hi-tree.

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33

Bernstein, Herbert J., and Lawrence C. Andrews. "Acceleratingk-nearest-neighbor searches." Journal of Applied Crystallography 49, no. 5 (August 10, 2016): 1471–77. http://dx.doi.org/10.1107/s1600576716011353.

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The search for whichkpoints are closest to a given probe point in a space ofNknown points, the `k-nearest-neighbor' or `KNN' problem, is a computationally challenging problem of importance in many disciplines, such as the design of numerical databases, analysis of multi-dimensional experimental data sets, multi-particle simulations and data mining. A standard approach is to preprocess the data into a tree and make use of the triangle inequality to prune the search time to the order of the logarithm ofNfor a single nearest point in a well balanced tree. All known approaches suffer from the `curse of dimensionality', which causes the search to explore many more branches of the tree than one might wish as the dimensionality of the problem increases, driving search times closer to the order ofN. Looking forknearest points can sometimes be done in approximately the time needed to search for one nearest point, but more often it requiresksearches because the results are distributed widely. The result is very long search times, especially when the search radius is large andkis large, and individual distance calculations are very expensive, because the same probe-to-data-point distance calculations need to be executed repeatedly as the top of the tree is re-explored. Combining two acceleration techniques was found to improve the search time dramatically: (i) organizing the search into nested searches in non-overlapping annuli of increasing radii, using an estimation of the Hausdorff dimension applicable to this data instance from the results of earlier annuli to help set the radius of the next annulus; and (ii) caching all distance calculations involving the probe point to reduce the cost of repeated use of the same distances. The result of this acceleration in a search of the combined macromolecular and small-molecule data in a combined six-dimensional database of nearly 900 000 entries has been an improvement in the overall time of the searches by one to two orders of magnitude.

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Mohan, Kondrahalli C., and Peter Willett. "Nearest neighbour searching in serial files using text signatures." Journal of Information Science 11, no. 1 (July 1985): 31–39. http://dx.doi.org/10.1177/016555158501100105.

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A nearest neighbour search procedure is described for use with serial files of textual data. The procedure involves the grouping of records into blocks, each of which is characterised by a fixed-length bit string. A comparable query bit string may then be matched against each of these bit strings, and an upper bound calculation used to identify those blocks which need to be inspected in detail if the document that is most similar to the query is to be identified. Experiments with three small collections of documents and queries are used to test the efficiency of the approach. The experiments show that reduc tions in computation are possible, although the precise savings are crucially dependent upon a range of factors including the frequency characteristics of the documents and queries, the similarity coefficients, and the sizes of the bit strings and of the blocks.

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35

STEWART, MARK, and PETER WILLETT. "NEAREST NEIGHBOUR SEARCHING IN BINARY SEARCH TREES: SIMULATION OF A MULTIPROCESSOR SYSTEM." Journal of Documentation 43, no. 2 (February 1987): 93–111. http://dx.doi.org/10.1108/eb026803.

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36

HUSADA, MILDA GUSTIANA, DINA BUDHI UTAMI, and IWAN ZAR. "Karakteristik Metode Sift dalam Aplikasi Sistem Pengenalan Motif Batik." MIND Journal 4, no. 2 (December 10, 2019): 122–31. http://dx.doi.org/10.26760/mindjournal.v4i2.42-51.

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Pada kajian ini dibahas penerapan CBIR yaitu cara perolehan temu balik (retreival) objek citra melalui proses pembandingan antara citra uji terhadap citra latih yang dikumpulkan dalam suatu database. Proses membandingan citra berlandaskan pada tanda-tanda (ciri) yang dimiliki diantara citra tersebut. Tanda-tanda yang digunakan pada cara CBIR yaitu berdasarkan kemiripan warna, bentuk, dan tekstur. Pada makalah ini metode SIFT digunakan untuk mendapatkan dan mendeskripsikan fitur-fitur lokal yang ada pada citra. Fitur citra latih dan citra uji yang diperoleh kemudian dibandingkan dengan menggunakan Nearest Neighbour Search untuk memperoleh tingkat kemiripan (proses image matching). Pengujian dilakukan pada citra yang diperoleh melalui kamera dan citra yang sudah berupa data digital. Berdasarkan pengujian, nilai precision dan recall untuk citra uji yang diambil melalui kamera berturut-turut adalah 64% dan 12,8%, sedangkan untuk citra uji dari digital dioleh adalah 84% dan 16.8%. Kata kunci: CBIR, SIFT, Image Matching, Nearest Neighbor Search

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37

Lakemond, Ruan, Clinton Fookes, and Sridha Sridharan. "Fast Exact Nearest Neighbour Matching in High Dimensions Using d-D Sort." ISRN Machine Vision 2013 (February 17, 2013): 1–8. http://dx.doi.org/10.1155/2013/405680.

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Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k nearest neighbour matching, but are only marginally more effective than linear search when performing exact matching in high-dimensional image descriptor data. This paper presents several improvements to linear search that allows it to outperform existing methods and recommends two approaches to exact matching. The first method reduces the number of operations by evaluating the distance measure in order of significance of the query dimensions and terminating when the partial distance exceeds the search threshold. This method does not require preprocessing and significantly outperforms existing methods. The second method improves query speed further by presorting the data using a data structure called d-D sort. The order information is used as a priority queue to reduce the time taken to find the exact match and to restrict the range of data searched. Construction of the d-D sort structure is very simple to implement, does not require any parameter tuning, and requires significantly less time than the best-performing tree structure, and data can be added to the structure relatively efficiently.

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38

Gassner, M., and B. Brabec. "Nearest neighbour models for local and regional avalanche forecasting." Natural Hazards and Earth System Sciences 2, no. 3/4 (December 31, 2002): 247–53. http://dx.doi.org/10.5194/nhess-2-247-2002.

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Abstract. This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF). Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

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Yan Chen, and Yunjun Gao. "Ranked Continuous Visible Nearest Neighbor Search." Journal of Convergence Information Technology 5, no. 8 (October 31, 2010): 157–64. http://dx.doi.org/10.4156/jcit.vol5.issue8.16.

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Soleymani, M., and S. Morgera. "An Efficient Nearest Neighbor Search Method." IEEE Transactions on Communications 35, no. 6 (June 1987): 677–79. http://dx.doi.org/10.1109/tcom.1987.1096830.

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Jégou, H., M. Douze, and C. Schmid. "Product Quantization for Nearest Neighbor Search." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 1 (January 2011): 117–28. http://dx.doi.org/10.1109/tpami.2010.57.

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Yufei Tao and Cheng Sheng. "Fast Nearest Neighbor Search with Keywords." IEEE Transactions on Knowledge and Data Engineering 26, no. 4 (April 2014): 878–88. http://dx.doi.org/10.1109/tkde.2013.66.

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Chen, Lu, Yunjun Gao, Gang Chen, and Haida Zhang. "Metric All-k-Nearest-Neighbor Search." IEEE Transactions on Knowledge and Data Engineering 28, no. 1 (January 1, 2016): 98–112. http://dx.doi.org/10.1109/tkde.2015.2453954.

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Tellez, E. S., G. Ruiz, and E. Chavez. "Singleton indexes for nearest neighbor search." Information Systems 60 (August 2016): 50–68. http://dx.doi.org/10.1016/j.is.2016.03.003.

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Miao, Xiaoye, Yunjun Gao, Gang Chen, Baihua Zheng, and Huiyong Cui. "Processing Incomplete k Nearest Neighbor Search." IEEE Transactions on Fuzzy Systems 24, no. 6 (December 2016): 1349–63. http://dx.doi.org/10.1109/tfuzz.2016.2516562.

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Otepka, J., G. Mandlburger, W. Karel, B. Wöhrer, C. Ressl, and N. Pfeifer. "A FRAMEWORK FOR GENERIC SPATIAL SEARCH IN 3D POINT CLOUDS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (June 17, 2021): 35–42. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-35-2021.

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Abstract. Modern data acquisition with active or passive photogrammetric imaging techniques generally results in 3D point clouds. Depending on the acquisition or processing method, the spacing of the individual points is either uniform or irregular. In the latter case, the neighbourhood definition like for digital images (4- or 8-neighbourhood, etc.) cannot be applied. Instead, analysis requires a local point neighbourhood. The local point neighbourhood with conventional k-nearest neighbour or fixed distance searches often produce sub-optimal results suffering from the inhomogeneous point distribution. In this article, we generalize the neighbourhood definition and present a generic spatial search framework which explicitly deals with arbitrary point patterns and aims at optimizing local point selection for specific processing tasks like interpolation, surface normal estimation and point feature extraction, spatial segmentation, and such like. The framework provides atomic 2D and 3D search strategies, (i) k-nearest neighbour, (ii) region query, (iii) cell based selection, and (iv) quadrant/octant based selection. It allows to freely combine the individual strategies to form complex, conditional search queries as well as specifically tailored point sub-selection. The benefits of such a comprehensive neighbourhood search approach are showcased for feature extraction and surface interpolation of irregularly distributed points.

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Park, Youngki, Heasoo Hwang, and Sang-goo Lee. "Query-specific signature selection for efficient k-nearest neighbour approximation." Journal of Information Science 43, no. 4 (May 1, 2016): 440–57. http://dx.doi.org/10.1177/0165551516644176.

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Finding k-nearest neighbours ( k-NN) is one of the most important primitives of many applications such as search engines and recommendation systems. However, its computational cost is extremely high when searching for k-NN points in a huge collection of high-dimensional points. Locality-sensitive hashing (LSH) has been introduced for an efficient k-NN approximation, but none of the existing LSH approaches clearly outperforms others. We propose a novel LSH approach, Signature Selection LSH (S2LSH), which finds approximate k-NN points very efficiently in various datasets. It first constructs a large pool of highly diversified signature regions with various sizes. Given a query point, it dynamically generates a query-specific signature region by merging highly effective signature regions selected from the signature pool. We also suggest S2LSH-M, a variant of S2LSH, which processes multiple queries more efficiently by using query-specific features and optimization techniques. Extensive experiments show the performance superiority of our approaches in diverse settings.

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Sato, Hideki, and Ryoichi Narita. "Approximate search algorithm for aggregate k-nearest neighbour queries on remote spatial databases." International Journal of Knowledge and Web Intelligence 4, no. 1 (2013): 3. http://dx.doi.org/10.1504/ijkwi.2013.052722.

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Nguyen, Hai Thanh, Linh Dan Vo, and Thien Thanh Tran. "Approximate Nearest Neighbour-based Index Tree: A Case Study for Instrumental Music Search." Applied Computer Systems 28, no. 1 (June 1, 2023): 156–62. http://dx.doi.org/10.2478/acss-2023-0015.

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Abstract Many people are interested in instrumental music. They may have one piece of song, but it is a challenge to seek the song because they do not have lyrics to describe for a text-based search engine. This study leverages the Approximate Nearest Neighbours to preprocess the instrumental songs and extract the characteristics of the track in the repository using the Mel frequency cepstral coefficients (MFCC) characteristic extraction. Our method digitizes the track, extracts the track characteristics, and builds the index tree with different lengths of each MFCC and dimension number of vectors. We collected songs played with various instruments for the experiments. Our result on 100 pieces of various songs in different lengths, with a sampling rate of 16000 and a length of each MFCC of 13, gives the best results, where accuracy on the Top 1 is 36 %, Top 5 is 4 %, and Top 10 is 44 %. We expect this work to provide useful tools to develop digital music e-commerce systems.

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ZHAO, GENG, KEFENG XUAN, DAVID TANIAR, and BALA SRINIVASAN. "INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS." Journal of Interconnection Networks 09, no. 04 (December 2008): 455–70. http://dx.doi.org/10.1142/s0219265908002382.

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Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objects one by one from the query point. This approach is based on expanding the network from the query point, keeping the results in a query set and updating the query set when reaching network intersection or interest objects. The basic theory of this approach is Dijkstra's algorithm and the Incremental Network Expansion (INE) algorithm. Our experiment verified the applicability of the proposed approach to solve the queries, which involve finding incremental k nearest neighbor.

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