Adaptive nearest neighbor search for relevance feedback in large image databases

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.

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Adaptive bit allocation hashing for approximate nearest neighbor search

Neurocomputing ◽

10.1016/j.neucom.2014.10.042 ◽

2015 ◽

Vol 151 ◽

pp. 719-728 ◽

Cited By ~ 4

Author(s):

Qin-Zhen Guo ◽

Zhi Zeng ◽

Shuwu Zhang

Keyword(s):

Nearest Neighbor ◽

Nearest Neighbor Search ◽

Bit Allocation ◽

Approximate Nearest Neighbor Search ◽

Approximate Nearest Neighbor ◽

Neighbor Search

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PCT: Point cloud transformer

Computational Visual Media ◽

10.1007/s41095-021-0229-5 ◽

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.

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