scholarly journals Improving Speed and Accuracy of Image Retrieval using Elastic Search and Features Nearest Neighbor Search

2019 ◽  
Vol 8 (3) ◽  
pp. 909-913
Keyword(s):  
Image Retrieval ◽  
Nearest Neighbor ◽  
Data Retrieval ◽  
Nearest Neighbor Search ◽  
Neighbor Search ◽  
Current Term ◽  
Speed And Accuracy ◽  
Coordinate Gauge

A developing interest had shown as late in structure closest neighbor search arrangements inside Elastic search–one of the most well-known full-content web indexes. In this paper, we focus explicitly around Elastic search and Features Nearest Neighbor search (ESFNNS), which accomplishes sensitive speedups over the current term coordinate gauge. Features Nearest Neighbor search performs the image retrieval, which integrates the features of color, shape, and texture. This will engage an Elastic search with the capacity of quick data retrieval and accuracy when compared to the FENSHSES method.

Probabilistic cost model for nearest neighbor search in image retrieval

2012 ◽  
Vol 116 (9) ◽  
pp. 991-998 ◽  
Author(s):  
Kunho Kim ◽  
Mohammad K. Hasan ◽  
Jae-Pil Heo ◽  
Yu-Wing Tai ◽  
Sung-eui Yoon
Keyword(s):  
Image Retrieval ◽  
Nearest Neighbor ◽  
Cost Model ◽  
Nearest Neighbor Search ◽  
Neighbor Search

scholarly journals Learning Deep Unsupervised Binary Codes for Image Retrieval

2018 ◽  
Author(s):  
Junjie Chen ◽  
William K. Cheung ◽  
Anran Wang
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Multimedia Retrieval ◽  
Binary Codes ◽  
Linear Transformations ◽  
Neighbor Search ◽  
Compact Representations ◽  
The Difference

Hashing is an efficient approximate nearest neighbor search method and has been widely adopted for large-scale multimedia retrieval. While supervised learning is more popular for the data-dependent hashing, deep unsupervised hashing methods have recently been developed to learn non-linear transformations for converting multimedia inputs to binary codes. Most of existing deep unsupervised hashing methods make use of a quadratic constraint for minimizing the difference between the compact representations and the target binary codes, which inevitably causes severe information loss. In this paper, we propose a novel deep unsupervised method called DeepQuan for hashing. The DeepQuan model utilizes a deep autoencoder network, where the encoder is used to learn compact representations and the decoder is for manifold preservation. To contrast with the existing unsupervised methods, DeepQuan learns the binary codes by minimizing the quantization error through product quantization technique. Furthermore, a weighted triplet loss is proposed to avoid trivial solution and poor generalization. Extensive experimental results on standard datasets show that the proposed DeepQuan model outperforms the state-of-the-art unsupervised hashing methods for image retrieval tasks.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

scholarly journals PCT: Point cloud transformer

2021 ◽  
Vol 7 (2) ◽  
pp. 187-199
Author(s):  
Meng-Hao Guo ◽  
Jun-Xiong Cai ◽  
Zheng-Ning Liu ◽  
Tai-Jiang Mu ◽  
Ralph R. Martin ◽  
...  
Keyword(s):  
Language Processing ◽  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
Nearest Neighbor Search ◽  
Local Context ◽  
Irregular Domain ◽  
Cloud Processing ◽  
Neighbor Search ◽  
Farthest Point

AbstractThe irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning. To better capture local context within the point cloud, we enhance input embedding with the support of farthest point sampling and nearest neighbor search. Extensive experiments demonstrate that the PCT achieves the state-of-the-art performance on shape classification, part segmentation, semantic segmentation, and normal estimation tasks.

scholarly journals Authenticated Multistep Nearest Neighbor Search

2011 ◽  
Vol 23 (5) ◽  
pp. 641-654 ◽  
Author(s):  
Stavros Papadopoulos ◽  
Lixing Wang ◽  
Yin Yang ◽  
Dimitris Papadias ◽  
Panagiotis Karras
Keyword(s):  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search
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