Nearest-neighbor Strategy Research Articles

Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model

Objective. Brain decoding is a field of computational neuroscience that aims to infer mental states or internal representations of perceptual inputs from measurable brain activity. This study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. Approach. We use several functional magnetic resonance imaging (fMRI) datasets of natural images as stimuli and create a deep learning decoding pipeline inspired by the bottom-up and top-down processes in human vision. Our pipeline includes a linear brain-to-feature model that maps fMRI activity to semantic visual stimuli features. We assume that the brain projects visual information onto a space that is homeomorphic to the latent space of last layer of a pretrained neural network, which summarizes and highlights similarities and differences between concepts. These features are categorized in the latent space using a nearest-neighbor strategy, and the results are used to retrieve images or condition a generative latent diffusion model to create novel images. Main results. We demonstrate semantic classification and image retrieval on three different fMRI datasets: Generic Object Decoding (vision perception and imagination), BOLD5000, and NSD. In all cases, a simple mapping between fMRI and a deep semantic representation of the visual stimulus resulted in meaningful classification and retrieved or generated images. We assessed quality using quantitative metrics and a human evaluation experiment that reproduces the multiplicity of conscious and unconscious criteria that humans use to evaluate image similarity. Our method achieved correct evaluation in over 80% of the test set. Significance. Our study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. The results demonstrate that measurable neural correlates can be linearly mapped onto the latent space of a neural network to synthesize images that match the original content. These findings have implications for both cognitive neuroscience and artificial intelligence.

Supervised Manifold Learning Based on Multi-Feature Information Discriminative Fusion within an Adaptive Nearest Neighbor Strategy Applied to Rolling Bearing Fault Diagnosis.

Rolling bearings are a key component for ensuring the safe and smooth operation of rotating machinery and are very prone to failure. Therefore, intelligent fault diagnosis research on rolling bearings has become a crucial task in the field of mechanical fault diagnosis. This paper proposes research on the fault diagnosis of rolling bearings based on an adaptive nearest neighbor strategy and the discriminative fusion of multi-feature information using supervised manifold learning (AN-MFIDFS-Isomap). Firstly, an adaptive nearest neighbor strategy is proposed using the Euclidean distance and cosine similarity to optimize the selection of neighboring points. Secondly, three feature space transformation and feature information extraction methods are proposed, among which an innovative exponential linear kernel function is introduced to provide new feature information descriptions for the data, enhancing feature sensitivity. Finally, under the adaptive nearest neighbor strategy, a novel AN-MFIDFS-Isomap algorithm is proposed for rolling bearing fault diagnosis by fusing various feature information and classifiers through discriminative fusion with label information. The proposed AN-MFIDFS-Isomap algorithm is validated on the CWRU open dataset and our experimental dataset. The experiments show that the proposed method outperforms other traditional manifold learning methods in terms of data clustering and fault diagnosis.

Comparing the risk of serious infections in patients with and without MS: A German claims data analysis

Research suggests that serious infections (SIs), comorbidities, and advanced disability represent key drivers of early death in people with Multiple Sclerosis (pwMS). Nevertheless, further research is warranted to better characterize and quantify the risk of SI among pwMS compared to the general population. Our study consisted of a retrospective analysis of claims data provided by a German statutory health insurance fund, AOK PLUS, covering 3.4 million individuals in Saxony and Thuringia from 01/01/2015-31/12/2019. A propensity score (PS) matching method was used to compare the incidence of SIs among people with and without MS. PwMS were required to have ≥1 inpatient or ≥2 confirmed outpatient diagnoses of MS (ICD-10 G35) from a neurologist from 01/01/2016-31/12/2018, while people from the general population could not have any inpatient/outpatient codes for MS during the entire study period. The index date was defined as the first observed MS diagnosis or, in the case of the non-MS cohort, a randomly assigned date within the inclusion period. For both cohorts a PS was assigned, corresponding with their probabilistic likelihood of having MS based on observable factors including patient characteristics, comorbidities, medication use and other variables. People with and without MS were matched using a 1:1 nearest neighbor strategy. An exhaustive list of ICD-10 codes was created in association with 11 main SI categories. SIs were those recorded as the main diagnosis during an inpatient stay. ICD-10 codes from the 11 main categories were sorted into smaller classification units, used to distinguish between infections. A 60-day threshold for measuring new cases was defined to account for the potential risk of re-infection. Patients were observed until the end of the study period (31/12/2019) or death. Cumulative incidence, incidence rates (IRs) and IR ratios (IRRs) were reported during follow-up and at 1-, 2- and 3-years post-index. A total of 4250 and 2,098,626 patients were included in the unmatched cohorts of people with and without MS. Ultimately, one match was identified for all 4,250 pwMS, corresponding with a final population of 8,500 patients. On average, patients were 52.0/52.2 years in the matched MS/non-MS cohorts; the gender breakdown was 72% female. Overall, IRs of SIs per 100 patient years (PY) were higher in pwMS than in those without MS (1 year: 7.6vs. 4.3; 2 years: 7.1vs. 3.8; 3 years: 6.9vs. 3.9). During follow-up, the most common infection types in pwMS were of a bacterial/parasitic origin (2.3 per 100 PY), followed by respiratory (2.0) and genitourinary (1.9) infections. Respiratory infections were most common in patients without MS (1.5 per 100 PY). Differences in the IRs of SIs were statistically significant (p<0.01) at each measurement window, with IRRs ranging from 1.7-1.9. PwMS had a higher risk of hospitalized genitourinary infections (IRR: 3.3-3.8) and bacterial/parasitic infections (2.0-2.3). The incidence of SIs is much higher in pwMS, than comparators from the general population in Germany. Differences in hospitalized infection rates were largely driven by higher levels of bacterial/parasitic and genitourinary infections in the MS population.

Switching synthesizing-incorporated and cluster-based synthetic oversampling for imbalanced binary classification

Oversampling is a popular yet useful method to fulfill the binary classification of imbalanced data, however many existing results of oversampling are very likely to generate redundant/unsafe/noise samples due primarily to the inadequate consideration of the data distribution. To address this issue, we propose a novel oversampling approach, namely Switching Synthesizing-Incorporated and Cluster-Based Synthetic Oversampling (SSI-CBSO). The core idea of SSI-CBSO is four-fold: (1) noise samples are removed by using K nearest neighbor strategy and Fuzzy C-Means clustering is adopted for the filtered data in the minority class; (2) the number of samples that need to be synthesized is adaptively assigned to each cluster concerning the inter-class distance and the intra-cluster similarity; (3) to better reflect the data distribution, a new method in terms of the concept of the hypersphere is put forward to measure the cluster density in a high dimensional; and (4) a new principle based on the Mahalanobis distance is provided for a better selection of the target sample. Then, a switching synthesizing strategy is established to guarantee the safety of the synthesized samples. Finally, experiments on 13 binary imbalanced data sets by using five evaluation metrics with four classifiers verify that our proposed SSI-CBSO approach can obtain desirable results.

Robust Face Recognition Algorithm Based on Multidirectional Log-Gabor Features

In order to improve the robustness to variable factors such as illumination, expression and pose, a face recognition method based on multi-directional Log-Gabor feature map sparse representation is proposed. First, conduct multi-directional and multi-scale Log-Gabor transformation on the face image, and then fuse the Log-Gabor features of different scales in the same direction to obtain a multi-directional feature map. Then, extract Gist features from the fused feature map of each direction and give them adaptive weights. Then, connect the adaptive weighted Gist features of all directional feature maps to form a feature vector of the face image, Finally, sparse representation classification method is used to realize face recognition. Face recognition experiments were carried out on WIDER FACE dataset and the MAFA occlusion face dataset, and the identification accuracy of the proposed method is compared with SRC, NMR, Gabor NLDA NN and Gabor NLDA SVM. The experimental results show that the method in this paper can effectively describe the local face information by using multi-directional Log Gabor feature map (MGFM) to establish the feature representation of the face image, and the improved cooperative representation classification algorithm using adaptive K nearest neighbor strategy can achieve higher recognition accuracy.

Collaborative Truck-Drone Routing for Contactless Parcel Delivery During the Epidemic

The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic areas. We formulate a mixed-integer programming model that minimizes the delivery time, with the consideration of the energy consumption model of drones. To solve CRP-T&D, we develop an improved variable neighborhood descent (IVND) that combines the Metropolis acceptance criterion of Simulated Annealing (SA) and the tabu list of Tabu Search (TS). Meanwhile, the integration of K-means clustering and Nearest neighbor strategy is applied to generate the initial solution. To evaluate the performance of IVND, experiments are conducted by comparing IVND with VND, SA, TS, variants of VND, and large neighborhood search (LNS) on instances with different scales. Several critical factors are tested to verify the robustness of IVND. Moreover, the experimental results on a practical instance further demonstrate the superior performance of IVND.

Hybrid Ant Colony Algorithm Using Improved Circle Strategy for TSP Problem

Based on the problems existing in the traditional ant colony algorithm in solving the traveling salesman problem, a hybrid ant colony algorithm combining the improved circle strategy and the ant colony algorithm is proposed. In the proposed hybrid ant colony algorithm, an improved circle strategy is used to optimize the solution obtained by the ant colony algorithm, so as to improve the search efficiency and search ability. At the same time, the uniform design method is used to find the optimal parameter combination of the algorithm. The improved circle strategy is based on the nearest neighbor strategy to optimize the solution obtained by the ant colony algorithm into a better solution. This paper uses 8 standard instances in the TSPLIB standard library to experimentally verify the algorithm. The experimental results show that the proposed hybrid ant colony algorithm can effectively improve the convergence ability of the algorithm, obtain higher quality solutions, and have better optimization ability and stability for solving TSP problems.

A novel hierarchical feature selection method based on large margin nearest neighbor learning

The complexity of data has been increasing not only in the dimension of the data, but also in the interrelated class relationships. Pre-defined hierarchical structure of classes helps to model such relationships in a way, which divides the whole classification problem into a set of subtasks. Feature selection, known as a data preprocessing technique, aims to selecting useful feature to improve the performance of learning algorithms. However, the issue of close class relationships, especially under the same parent node in the hierarchical structure, is adverse to the feature selection. In this paper, we introduce a novel distance metric based feature selection method with large margin nearest neighbor strategy for the hierarchical classification problems. Different from other algorithms, our proposed method is to project the data into a low-dimensional feature space for learning correct metrics, and the weights of features will be indicated in the transformation matrix. A sparsity regularization and iterative optimization ensure that superior feature subsets can be selected. Experimental evaluation on several datasets with hierarchical class structure demonstrates that the proposed approach is comparable or better than some other state-of-the-art hierarchical feature selection methods.

Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multi-view data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.

Adaptive specialization for spatial memory does not improve route efficiency: Comparing the ability of Clark's nutcrackers (Nucifraga columbiana) and pigeons (Columba livia) to solve traveling salesperson problems.

An important question in comparative cognition is whether animals are capable of planning ahead. Todd and Hills (Current Directions in Psychological Science, 29(3), 309-315, 2020) recently suggested that the ability to plan and choose internally may have scaffolded upon the cognitive mechanisms required by animals to search among patchy resources in their external environment. The traveling salesperson problem (TSP) is a spatial optimization problem in which a traveler is faced with the task of finding the best route from a start location to two or more destinations or targets. The Clark's nutcracker (Nucifraga columbiana) is a food-storing corvid with a highly specialized spatial memory. Spatial memory would appear to be deeply rooted in the cognitive mechanisms required for choosing efficiently among multiple alternative routes during a TSP. If so, then species like nutcrackers that are more dependent upon spatial memory for survival may have a greater ability to plan ahead or choose more efficiently among different route options than species that have less selective pressure for remembering the location of food, like pigeons. We examined the ability of nutcrackers to solve TSPs using the same procedures and target configurations as in our past research (Gibson, Wilkerson, & Kelly, Animal Cognition, 15, 379-391, 2012) to explore if nutcrackers can efficiently solve TSPs and how their route solutions compare with those of pigeons. Nutcrackers did not display an advantage in route efficiency and performed comparably to pigeons. Both species tended to prefer a nearest-neighbor strategy to more globally efficient routes. Having a more robust spatial memory may not improve the ability of animals to determine routes to multiple locations.

Dynamic reproductive ant colony algorithm based on piecewise clustering

To address the lack of convergence speed and diversity of Ant Colony Optimization (ACO), a dynamic reproductive ant colony algorithm based on piecewise clustering (RCACS) is proposed to optimize the problems. First, the data is segmented by the clustering algorithm, and the exit and entrance of each cluster are obtained by the nearest neighbor strategy. The algorithm traversed all points of the cluster to find an unclosed shortest path according to the exit and entrance. Many fragment paths eventually merge into a full TSP. This strategy can accelerate the convergence speed of the algorithm and help it get higher accuracy. Second, when the algorithm stagnates, the dynamic regeneration mechanism based on feature transfer will transfer the excellent features of the mother-ants to the child-ants, so that it can further explore the neighborhood of the current optimal solution and help the algorithm jump out of the local optimum. From the results of simulation experiments and the rank-sum test, it can be found that the improved algorithm can effectively improve the diversity and accuracy of the algorithm, especially when solving large-scale problems.

Positional deviation detection of silicone caps on FPCB

PurposeThe purpose of this paper is to propose a defect detection method of silicone caps positional deviation on flexible printed circuit board (FPCB) of keyboard based on automatic optical inspection.Design/methodology/approachFirst, the center of silicone caps of target keyboard FPCB image was extracted as feature points for generating the feature image which is used for registration rigidly with the reference feature image generated from the CAD drawings. Then, a flexible image registration method based on the surrounding-control-center B-splines (SCCB) strategy was proposed, which could correct the flexible deformation of the image generated by FPCB substrate while keeping the pasting deviation information about silicone caps unchanged. Finally, on this basis, a nearest neighbor strategy was proposed to detect the positional deviation of silicone caps.FindingsExperimental results show that the proposed method can effectively detect the positional deviation defect of silicone caps. The G-mean value of the proposed method is 0.941746, which is 0.3 higher compared to that of similar research.Originality/valueThis paper presents a method to detect positional deviation defect of silicone caps on keyboard FPCB. Different from the classic B-spline image registration method, the proposed SCCB method used the neighborhood information of the pixel to be registered selectively to calculate the displacement vector needed for its registration, which overcame the problem that the silicone cap pasting deviation information disappears with the correction of the flexible deformation of the image.

TangleCV

Connected vehicles are set to define the future of transportation; however, this upcoming technology continues to be plagued with serious security risks. If these risks are not addressed in a timely fashion, then they could threaten the adoption and success of this promising technology. This article deals with a specific class of attacks in connected vehicles, namely tampering attacks caused due to compromise of on-board sensors. Current centralized solutions that employ trusted infrastructure to protect against adversarial manipulation of information cannot validate the correctness of the shared data and do not scale well. To overcome these issues, decentralized protection mechanisms by means of blockchain technology have emerged as a promising research direction. However, current permission-less, linear blockchain-based solutions have low transaction performance and high computational cost, thereby making it difficult to adopt them for security in connected vehicles. In this article, we present TangleCV, a directed acyclic graph–based distributed ledger technique for connected vehicles to address data tampering threats in connected vehicular networks. We describe new validation steps, tip selection strategies, and cumulative weight definition for TangleCV that not only meets the timing constraints of the connected vehicular networks but also secures the network against threats due to tampering attacks. We describe how the reputation of the network is established in TangleCV using trust factors calculated on the basis of ability, integrity, and benevolence of the nodes in the network. We present numerical results that demonstrate that the average value of the time to first approval decreases by more than 70% as the network evolves from a low load to a high load in the case of the nearest neighbor strategy. We observe that more than 60% of the nodes are approved in a low-load network and this number increases to 80% in a high-load network for the nearest neighbor strategy. The standard deviation of error measurements for nodes experiencing tampering attack is around 60% higher as compared to nodes that do not experience such an attack.

Near-optimal large-scale k-medoids clustering

The k-medoids (k-median) problem is one of the best known unsupervised clustering problems. Due to its complexity, finding high-quality solutions for huge-scale datasets remains extremely challenging. The application of many approaches finding optimal or quality solutions is limited to only small and medium-size instances. On the other hand, many parallel, distributed algorithms that can handle huge-scale datasets usually provide very poor solutions. In this paper, we develop a first parallel, distributed primal–dual heuristic algorithm for the k-medoids problem. Its main component is a very efficient parallel subgradient column generation that solves a Lagrangian dual problem and finds a tight bound on solution quality. High-quality solutions are then produced by a parallel core selection technique. We considerably reduce computational burden and memory load by employing a nearest neighbor strategy to approximate the dissimilarity matrix. We demonstrate that our algorithm finds very close to optimal solutions, confirmed by the tightness of dual bounds, of instances that are much larger than those considered in the literature to date. Our experiments include clustering large-scale collections of face images into several thousand of clusters. We show that our approach outperforms parallel improved versions of the most popular k-medoids clustering algorithms, achieving nearly linear parallel speedup.

Feature points-based image registration between satellite imagery and aerial images of agricultural land

Rapid advancement in remote sensing sensors has resulted in an enormous increase in the use of satellite imagery (SI) and images taken from unmanned aerial vehicles (UAVs) in a wide range of remote sensing applications. These applications include urban planning, environment monitoring, map updating, change detection, and precision agriculture. This paper focuses on an agricultural application of SI and UAV images. SI-UAV images possess high temporal, textural, and intensity differences due to rapid changes in agricultural crops with the passage of time. Feature points such as scale invariant feature transform (SIFT), oriented FAST and rotated BRIEF (ORB), and speeded-up robust features (SURF) are not invariant to such differences and underperform in SI-UAV image registration. To deal with this problem, we propose a new method that combines the strength of nearest neighbor (NN) and brute force (BF) descriptor matching strategies to register SI?UAV images. The proposed method is named NN-BF. For NN-BF first corresponding feature point descriptor matches are identified between SI-UAV images of the training set with overlap error. Then the corresponding descriptors are matched with the descriptors of SI images of the test set with NN strategy. The resulting descriptor matches are then further matched with the descriptors of UAV images of the test set using BF strategy. Finally, the descriptor matches obtained are processed with RANSAC to remove outliers and estimate a homography for image registration. Experiments are performed on an agricultural land image dataset. The experimental results show that the NN-BF method improves SIFT, SURF, and ORB feature point performances and also outperforms recently proposed feature matching strategies for remote sensing images. SIFT on average obtains 6.1% and 18.9% better precision scores than SURF and ORB with NN-BF, respectively. SIFT also obtains lower root mean square error than SURF and ORB with NN-BF.

Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors

Bridges are critical components of highways ensuring traffic can efficiently travel over obstructions such as bodies of water, valleys, and other roads. Ensuring bridges are in sound structural condition is essential for safe and efficient highway operations. Structural health monitoring (SHM) systems designed to measure bridge responses have been developed to quantitatively track the health of bridges. More recently, SHM systems have also begun to integrate measurement of vehicular loads that create the responses measured. However, precise correlation of traffic loads to bridge responses remains a costly and technically difficult strategy. To address existing technical limitations, a cyber-physical system (CPS) framework is proposed to track truck loads in a highway corridor, to trigger SHM systems to record bridge responses, and to automate the linking of bridge response data with truck weights collected by weigh-in-motion (WIM) stations installed along the corridor but not collocated with the bridges. To link truck weights to bridge responses, computer vision methods based on convolutional neural networks (CNN) are used to automate the detection and reidentification of trucks using traffic cameras. The single-stage CNN object detector YOLO is trained using a customized dataset to identify trucks from camera images at each instrumentation site; high precision is obtained with the YOLO detector exceeding 95% average precision (AP) for an intersection over union (IOU) threshold of 0.75. To reidentify the same truck at different locations in the corridor, this study adopts a CNN-based encoder trained via a triplet network and a mutual nearest neighbor strategy using feature vectors extracted from images at each measurement location. The proposed reidentification method is implemented in the CPS cloud environment and obtains a F1-score of 0.97. The study also explores the triggering of bridge monitoring systems based on visual detection of trucks by a traffic camera installed upstream to the bridges. The triggering strategy proves to be highly efficient with 99% of the triggered data collection cycles capturing truck events at each bridge. To validate, the CPS architecture is implemented on a 20-mile highway corridor that has a WIM station already installed; four traffic cameras and two bridge SHM systems are installed along the corridor and integrated with a CPS architecture hosted on the cloud. In total, over 10,000 trucks are observed at all measurement locations over one year allowing peak bridge responses to be correlated to both measured truck weights and to one another.

A Fast Randomized Algorithm for the Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery

In the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), customers demanding both delivery and pickup operations have to be visited once by a single vehicle. In this work, we propose a fast randomized algorithm using a nearest neighbor strategy to tackle an extension of the VRPSPD in which the fleet of vehicles is heterogeneous. This variant is an NP-hard problem, which in practice makes it impossible to be solved to proven optimality for large instances. To evaluate the proposal, we use benchmark instances from the literature and compare our results to those obtained by a state-of-the-art algorithm. Our approach presents very competitive results, not only improving several of the known solutions, but also running in a shorter time.

Application on Cold Chain Logistics Routing Optimization Based on Improved Genetic Algorithm

As the rise of fresh e-supplier, cold chain logistic has become the hot topics in China. But due to its special timeliness, it is necessary to optimize its vehicle routing. Firstly, we construct a cold chain logistics vehicle routing optimization with soft time windows model. Secondly, as simple genetic algorithm has some shortcomings such as poor population diversity and slow convergence, we propose an improved genetic algorithm – seeker genetic algorithm. By combining the uncertainty reasoning behavior in the seeker optimization algorithm and the nearest neighbor strategy, we improve the mutation operator in the genetic algorithm. Finally, we solve the cold chain logistics vehicle routing optimization model with basic genetic algorithm and seeker genetic algorithm respectively. The results indicate that seeker genetic algorithm could find the path with lower cost.

Dynamic Reduction-Expansion Operator to Improve Performance of Genetic Algorithms for the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is an important routing problem within the transportation industry. However, finding optimal solutions for this problem is not easy due to its computational complexity. In this work, a novel operator based on dynamic reduction-expansion of minimum distance is presented as an initial population strategy to improve the search mechanisms of Genetic Algorithms (GA) for the TSP. This operator, termed as RedExp, consists of four stages: (a) clustering to identify candidate supply/demand locations to be reduced, (b) coding of clustered and nonclustered locations to obtain the set of reduced locations, (c) sequencing of minimum distances for the set of reduced locations (nearest neighbor strategy), and (d) decoding (expansion) of the reduced set of locations. Experiments performed on TSP instances with more than 150 nodes provided evidence that RedExp can improve convergence of the GA and provide more suitable solutions than other approaches focused on the GA’s initial population.

OPRCP: approximate nearest neighbor binary search algorithm for hybrid data over WMSN blockchain

In order to prevent sensitive data tampering in the application of security monitoring, intelligent traffic, and other sensitive Internet of Things, the research on WMSN (wireless multimedia sensor networks) application system based on blockchain and IPFS (InterPlanetary File System) is of great significance. However, WMSN data are characterized by high dimensionality, large scale, and multiple types, so it is challenging to search WMSN data efficiently over blockchain system. This paper proposed a novel One Permutation with Rotation and cross-polytope locality-sensitive hashing (OPRCP) method of approximate nearest neighbor binary query for querying binary hybrid data in the form of WMSN multimedia data (containing two hybrid types of data, such as image-text and image-audio). Firstly, a binary hybrid data index was built with the method of locality-sensitive hashing (LSH) to retain content similarity among original data objects for performing accurate queries. Secondly, the approximate nearest neighbor search strategy was used in place of the nearest neighbor strategy, to reduce querying time. Finally, a binary hybrid data model was employed to cope with multiple types of data in WMSN and carry out collaborative search of binary hybrid data. The experimental results show that compared with other mainstream methods, the proposed OPRCP method is widely adaptive to massive high-dimensional data in multiple types and can improve the accuracy of query results. The OPRCP method exhibits good performance, effectively saves resources, and reduces query time for a variety of datasets. It is an effective solution to the binary hybrid search of approximate neighbors, and it is applicable to the WMSN data search based on smart contracts in WMSN blockchain systems.