Method For Nearest Neighbor Search Research Articles

Large-scale DSM registration via motion averaging

Abstract. Generating wide-area digital surface models (DSMs) requires registering a large number of individual, and partially overlapped DSMs. This presents a challenging problem for a typical registration algorithm, since when a large number of observations from these multiple DSMs are considered, it may easily cause memory overflow. Sequential registration algorithms, although can significantly reduce the computation, are especially vulnerable for small overlapped pairs, leading to a large error accumulation. In this work, we propose a novel solution that builds the DSM registration task as a motion averaging problem: pair-wise DSMs are registered to build a scene graph, with edges representing relative poses between DSMs. Specifically, based on the grid structure of the large DSM, the pair-wise registration is performed using a novel nearest neighbor search method. We show that the scene graph can be optimized via an extremely fast motion average algorithm with O(N) complexity (N refers to the number of images). Evaluation of high-resolution satellite-derived DSM demonstrates significant improvement in computation and accuracy.

NNFSRR: Nearest Neighbor Feature Selection and Redundancy Removal Method for Nearest Neighbor Search in Microarray Gene Expression Data

INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing a pivotal role in the field of bioinformatics and biomedical research. High-dimensional gene expression datasets hold a wealth of information, but their effective utilization is hindered by the presence of irrelevant dimensions and noise. The challenge lies in extracting meaningful features from these datasets to enhance the accuracy of disease prediction and classification while maintaining computational efficiency.
 Feature selection is a crucial step in addressing these challenges, as it aims to identify and retain only the most informative characteristics from large high-dimensional microarray datasets. In the context of microarray gene expression data, characterized by its substantial dimensionality, selecting relevant features is essential for efficient nearest neighbor search, a fundamental component of various analytical tasks in bioinformatics and data mining.
 Existing feature selection methods in high-dimensional data often face issues related to the trade-off between search accuracy and computational efficiency. This paper introduces a novel approach, the Nearest Neighbor Feature Selection with Symmetrical Uncertainty-based Redundancy Removal (NNFSRR) method, designed to enhance the classification of microarray gene expression data through feature selection. The NNFSRR method focuses on reducing the dimensionality of the dataset by identifying and removing redundant features, allowing subsequent searches to operate solely on relevant dimensions.
 OBJECTIVES: The primary goal is to evaluate the NNFSRR method's effectiveness in improving nearest neighbor search in microarray gene expression datasets by reducing dimensionality. This method utilizes Symmetrical Uncertainty-based correlation between dimensions for feature selection and aims to enhance accuracy and efficiency compared to existing methods.
 METHODS: The NNFSRR method uses Symmetrical Uncertainty to identify and remove redundant features from microarray gene expression datasets. Reduced datasets are used for nearest neighbor search, improving accuracy and efficiency. Experiments are conducted using real-world datasets, and comparisons with existing methods are made based on search time and accuracy.
 RESULTS: The NNFSRR method demonstrates improved nearest neighbor search performance, outperforming basic brute force methods and existing feature selection techniques. Selected feature sets exhibit strong class associations while minimizing feature correlations, enhancing classification precision.
 CONCLUSION: In conclusion, the NNFSRR method presents a promising approach to address the challenges posed by high-dimensional gene expression data. It effectively reduces dimensionality, improves search accuracy, and enhances the efficiency of nearest neighbor search. Our experimental results demonstrate that this method outperforms existing techniques in terms of search time and accuracy, making it a valuable tool for applications in bioinformatics, data mining, pattern recognition, and biological information retrieval. The NNFSRR method holds the potential to advance our understanding of complex biological processes and support more accurate disease prediction and classification.

Association of late tricuspid regurgitation progression and intracardiac lead types in patients after primary implantation of cardiac electronic devices

Aim. To evaluate the factors and degree of progression of tricuspid regurgitation (TR) depending on the type of implanted electrode in patients after primary implantation of a cardiac implantable electronic device (CIED) in the late postoperative period.Methods. Case histories of 674 patients who underwent primary implantation of an implantable cardioverterdefibrillator (ICD) for the secondary prevention of sudden cardiac death for the period from 2009 to 2019 were analyzed and 75 case histories were selected. Using similar criteria, a comparison group was selected, who were implanted with a permanent pacemaker (PM) for sinus node dysfunction. For maximum comparability of the main and reference groups, pseudo-randomization was used using the nearest neighbor search method; 68 pairs were formed, comparable in terms of the main factors. Multiple logistic regression was used to analyze the predictors of progression of TR in the postoperative period.Results. In the preoperative period in the ICD group, 34 patients (50%) had a mild degree of TR, moderate and severe TR occurred in an equal number of cases 2 patients (2.9%) each. In the PM group, mild TR was detected in 24 cases (35.3%), moderate in 2.9% (n=2) of cases, respectively. In the postoperative period, predominantly mild TR was determined in both groups (in the ICD group in 67.6% (n=46), in the PM group in 48 patients (70.6%)).In the analysis of the general group of patients with implanted CIEDs (n=136), in the late postoperative period, an increase in the volume of both atria and the degree of TR was revealed. Left ventricular ejection fraction and end-diastolic volume didn’t have significant dynamics. All echocardiographic data obtained in the postoperative period were within the normal range.According to the results of multiple logistic regression, a history of coronary heart disease (CHD) has the greatest predictive power (hazard ratio 4.170; 95% confidence interval 1.751-9.933, p=0.001).Conclusion. TR in patients after primary implantation of a CIED in the late postoperative period progresses slightly, regardless of the type of right ventricular electrode. A long history of CHD is associated with the greatest risk of progression to tricuspid valve insufficiency.

A Hybrid Loop Closure Detection Method Based on Brain-Inspired Models

Various hippocampal-entorhinal-based models are used to construct brain-inspired simultaneous localization and mapping (SLAM) systems. Loop closure detection (LCD) is a critical process of SLAM systems for robots to relocalize themselves and correct accumulative errors. The existing LCD methods of brain-inspired SLAM systems cannot solve well with challenging or large-scale environments by hand-crafted features and brute force search strategy. In this article, we propose a hybrid LCD method, which is based on the convolutional neural network (CNN) features and the fly’s locality-sensitive hashing algorithm (Fly-LSH). CNN features can improve the reliability of image matching results, and the Fly-LSH, a nearest neighbor search method derived from the fruit fly olfactory circuit, is used to accelerate the image processing. We use multiple hash tables to make a balance between the image matching time and the accuracy of loop closures. The proposed method provides a general approach for SLAM systems to process visual cues, and has application in a hippocampal-entorhinal-based SLAM system. The experimental results verify that the proposed method enables the system to build cognitive maps with better robustness and efficiency.

Fast and robust loop-closure detection using deep neural networks and matrix transformation for a visual SLAM system

We investigate the loop-closure detection problem for a visual simultaneous localization and mapping (SLAM) system. Generally, a deep neural network is used to solve loop-closure detection problems. However, this method is time-consuming and occupies a large memory in practice, primarily owing to the high-dimensional nature of the data involved. Herein, we combine a pretrained convolutional neural network (CNN) model with a convolutional autoencoder to acquire a low-dimensional CNN vector as an image representation. Subsequently, the loop-closure problem is simulated by the single-image nearest-neighbor search method based on the cosine distance of the low-dimensional CNN vectors. In addition, to solve the perceptual aliasing problem in loop-closure detection, we empirically propose an efficient method for eliminating false positive loop closures by appropriately decomposing and reconstructing the similarity matrix. Furthermore, we verify through experiments that our method can effectively reduce the time and memory costs of loop-closure detection tasks while ensuring detection accuracy. Therefore, this method can provide a feasible solution to improve the real-time performance and robustness of visual SLAM systems.

Robust Underwater Localization Using Acoustic Image Alignment for Autonomous Intervention Systems

Acoustic imaging sonar can be used as important navigation sensors for underwater intervention systems as they consistently provide spatial information about the surrounding environment, even in limited visibility conditions. However, acoustic imaging sonars are known for high spatial ambiguity and low resolution of their measurements, which makes it challenging to obtain precise navigational information. This paper presents a novel localization method based on sensor fusion using an integrated IMU-DVL system and an acoustic imaging sonar. A sonar simulator is implemented and used to estimate the pose of the robot based on a single acoustic image, and it is formulated as an image alignment problem between a simulated acoustic image and an actual acoustic image. For this, an approximate nearest neighbor search method is employed for initial pose estimation, and a newly developed acoustic image alignment method is applied to obtain more accurate results in a continuous pose space. These methods are then combined to construct an integrated localization system using all the navigation sensors on the robot. Its feasibility and utility of the proposed approach is shown through an experimental validation in a test tank.

Linear and Deep Neural Network-Based Receivers for Massive MIMO Systems With One-Bit ADCs

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.

PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data.

Damage detection of 3D structures using nearest neighbor search method

Damage detection of 3D structures using nearest neighbor search method

Deep Multi-View Enhancement Hashing for Image Retrieval.

Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. However, large-scale high-speed retrieval through binary code has a certain degree of reduction in retrieval accuracy compared to traditional retrieval methods. We have noticed that multi-view methods can well preserve the diverse characteristics of data. Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance. In this paper, we propose a supervised multi-view hash model which can enhance the multi-view information through neural networks. This is a completely new hash learning method that combines multi-view and deep learning methods. The proposed method utilizes an effective view stability evaluation method to actively explore the relationship among views, which will affect the optimization direction of the entire network. We have also designed a variety of multi-data fusion methods in the Hamming space to preserve the advantages of both convolution and multi-view. In order to avoid excessive computing resources on the enhancement procedure during retrieval, we set up a separate structure called memory network which participates in training together. The proposed method is systematically evaluated on the CIFAR-10, NUS-WIDE and MS-COCO datasets, and the results show that our method significantly outperforms the state-of-the-art single-view and multi-view hashing methods.

A Supervoxel Approach to Road Boundary Enhancement From 3-D LiDAR Point Clouds

Rapid and accurate enhancement of road boundaries from terrestrial laser scanning (TLS) 3-D point clouds has been a challenging task in road infrastructure inventory. To address the challenge with a lack of ability to enhance object boundaries when the supervoxel number is less, this letter proposes a novel supervoxel segmentation algorithm framework for enhancing road boundaries from 3-D point clouds. First, we utilize radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest-neighbor search method to obtain the neighborhood information after partitioning points on octrees with seed points. Second, the iterative weighted least square algorithm and spatial structure judgment are used to segment point clouds based on seed points. Finally, an update method to adjust the supervoxel centroids is applied with surrounding information in the first part. To verify the excellent performance, we tested the proposed method on two publicly large-scale point clouds benchmarks—IQmulus and TerraMobilita (IQTM) and Semantic 3-D. The experimental results demonstrate that our approach achieved approximately 48.98% and 68.41% boundary recall higher than two existing classical methods in the street scene, and our running time is feasible and effective.

Discriminative deep metric learning for asymmetric discrete hashing

Thanks to its storage and computation efficiency, hashing as a kind of nearest neighbor search method can facilitate massive data processing in recent vision and learning studies. Particularly, deep supervised hashing methods have significantly improved the retrieval performance compared with non-deep supervised hashing methods. However, most existing deep supervised hashing methods approximate the similarity between two images with the hamming distance between the outputs of the same hash function, i.e., the symmetric strategy. Consequently, it is typically time-consuming to train these symmetric hashing methods, and these methods can hardly take full advantage of the supervised information in the large-scale database. In this paper, we propose a novel discriminative deep metric learning approach for asymmetric discrete hashing (ADMH) approach for supervised hashing learning. ADMH integrates an asymmetric strategy with a deep metric learning method to learn the hash function for the query images and the discrete hash codes for database images directly. More specifically, we train a deep neural network to extract the features of the query images. Subsequently, we introduce a metric learning scheme for learning the discrete discriminative hash codes of the database images directly. Finally, the feature learning procedure for generating the hash codes of the query images and the discrete coding procedure for generating the hash codes of the database images are integrated into an end-to-end learning framework. Extensive experiments on various benchmark datasets show that the proposed asymmetric deep hashing method outperforms the existing hashing methods.

Improved Search in Hamming Space Using Deep Multi-Index Hashing

Similarity-preserving hashing is a widely used method for nearest neighbor search in large-scale image retrieval tasks. Considerable research has been conducted on deep-network-based hashing approaches to improve the performance. However, the binary codes generated from deep networks may be not uniformly distributed over the Hamming space, which will greatly increase the retrieval time. To this end, we propose a deep-network-based multi-index hashing (MIH) for retrieval efficiency. We first introduce the MIH mechanism into the proposed deep architecture, which divides the binary codes into multiple substrings. Each substring corresponds to one hash table. Then, we add the two balanced constraints to obtain more uniformly distributed binary codes: 1) balanced substrings, where the Hamming distances of each substring are equal for any two binary codes and 2) balanced hash buckets, where the sizes of each bucket are equal. Extensive evaluations on several benchmark image retrieval data sets show that the learned balanced binary codes bring dramatic speedups and achieve comparable performance over the existing baselines.

Load and Load Dependent Friction Identification and Compensation of Electronic Non-Circular Gear Brake System

Control of the electronic non-circular gear brake (ENGB) involves challenges, including the non-linear variation of loads and the effect of friction, which is dependent upon load. The controller must be designed based on modelling information in order to enhance control performance. This study performed model identification of the ENGB system using a DOB-based model identification method. By employing the nearest neighbor search method, the even-odd disturbance was separated without the influence of hysteresis even in situations with low control precision. The accuracy of the resulting ENGB system model was validated through experiments. The self-energizing effect due to friction between the brake disc and pad within the mechanical system was also validated.

A Survey on Nearest Neighbor Search with Keywords

Today many applications use a new forms of query called as spatial keyword query which include finding objects closest to a specified location that contains specific set of keywords. For example, the nearest hotels to a specific location that contain facilities lunch and dry cleaning. Such query would ask for the hotels that are closest among those which provides facilities free lunch and dry all at the same time instead of considering all the hotels. Currently using IR2-tree is the best solution to such queries, which has a few deficiencies that seriously impact its efficiency. In this paper, we present a review on various methods used for NN search with keywords. whether a point is in rectangle or how two points are close from each other. Some new application allows users to browse objects based on both of their geometric coordinates and their associated texts. Such type of queries called as spatial keyword query. For example, if a search engine can be used to find nearest hotels to a specific location that contain facilities lunch and dry cleaning at the same time. From this query, we could first obtain entire hotels whose facilities contain the set of keywords, and then find the nearest one from the retrieved hotels. The major drawback of this approach is that, on the difficult input they do not provide real time answer. For example, from the query point the real neighbor lies quite far away, while all the closer neighbors are missing at least one of the query keywords. In the past years, the group of people has showed interest in studying keyword search in relational databases. Recently the attention has preoccupied to multidimensional data (5)(6). The best method for nearest neighbor search with keywords is because of Felipe et al. (5).

Margin-based two-stage supervised hashing for image retrieval

Similarity-preserving hashing is a widely used method for nearest neighbor search in large-scale image retrieval. Recently, supervised hashing methods are appealing in that they learn compact hash codes with fewer bits by incorporating supervised information. In this paper, we propose a new two-stage supervised hashing methods which decomposes the hash learning process into a stage of learning approximate hash codes followed by a stage of learning hash functions. In the first stage, we propose a margin-based objective to find approximate hash codes such that a pair of hash codes associating to a pair of similar (dissimilar) images has sufficiently small (large) Hamming distance. This objective results in a challenging optimization problem. We develop a coordinate descent algorithm to efficiently solve this optimization problem. In the second stage, we use convolutional neural networks to learn hash functions. We conduct extensive evaluations on several benchmark datasets with different kinds of images. The results show that the proposed margin-based hashing method has substantial improvement upon the state-of-the-art supervised or unsupervised hashing methods.

Instance-Aware Hashing for Multi-Label Image Retrieval.

Similarity-preserving hashing is a commonly used method for nearest neighbor search in large-scale image retrieval. For image retrieval, deep-network-based hashing methods are appealing, since they can simultaneously learn effective image representations and compact hash codes. This paper focuses on deep-network-based hashing for multi-label images, each of which may contain objects of multiple categories. In most existing hashing methods, each image is represented by one piece of hash code, which is referred to as semantic hashing. This setting may be suboptimal for multi-label image retrieval. To solve this problem, we propose a deep architecture that learns instance-aware image representations for multi-label image data, which are organized in multiple groups, with each group containing the features for one category. The instance-aware representations not only bring advantages to semantic hashing but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category. Extensive evaluations conducted on several benchmark data sets demonstrate that for both the semantic hashing and the category-aware hashing, the proposed method shows substantial improvement over the state-of-the-art supervised and unsupervised hashing methods.

NBC: An Efficient Hierarchical Clustering Algorithm for Large Datasets

Nearest neighbor search is a key technique used in hierarchical clustering and its computing complexity decides the performance of the hierarchical clustering algorithm. The time complexity of standard agglomerative hierarchical clustering is O(n3), while the time complexity of more advanced hierarchical clustering algorithms (such as nearest neighbor chain, SLINK and CLINK) is O(n2). This paper presents a new nearest neighbor search method called nearest neighbor boundary (NNB), which first divides a large dataset into independent subset and then finds nearest neighbor of each point in subset. When NNB is used, the time complexity of hierarchical clustering can be reduced to O(n log 2n). Based on NNB, we propose a fast hierarchical clustering algorithm called nearest-neighbor boundary clustering (NBC), and the proposed algorithm can be adapted to the parallel and distributed computing framework. The experimental results demonstrate that our algorithm is practical for large datasets.

A Nearest Neighbor Search Method for Image Matching Based on ORB

A Nearest Neighbor Search Method for Image Matching Based on ORB

Multiview alignment hashing for efficient image search.

Hashing is a popular and efficient method for nearest neighbor search in large-scale data spaces by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. For most hashing methods, the performance of retrieval heavily depends on the choice of the high-dimensional feature descriptor. Furthermore, a single type of feature cannot be descriptive enough for different images when it is used for hashing. Thus, how to combine multiple representations for learning effective hashing functions is an imminent task. In this paper, we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization, which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data. In particular, we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy. Since the raised problem is regarded as nonconvex and discrete, our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution. After finding the low-dimensional representation, the hashing functions are finally obtained through multivariable logistic regression. The proposed method is systematically evaluated on three data sets: 1) Caltech-256; 2) CIFAR-10; and 3) CIFAR-20, and the results show that our method significantly outperforms the state-of-the-art multiview hashing techniques.