8 results about "Locality-sensitive hashing" patented technology

The invention provides a multi-target accurate retrieval method and system in a security video, and relates to the field of interdisciplinary application, and the method is characterized in that the method comprises the following steps: carrying out the standardized preprocessing of an original security video, and deploying an improved YOLOv8 model on a preprocessed key frame to carry out the high-precision multi-target detection. The method has the advantages that the appearance, motion and semantic complementary features of the target are synchronously extracted by improving the YOLOv8 model, cross-frame association and trajectory modeling are realized by adopting an enhanced DeepSORT tracker, and a compact feature index database and a three-level progressive retrieval mechanism are constructed in combination with a locality sensitive hashing algorithm; according to the method, the multi-target retrieval precision and efficiency in massive videos are remarkably improved, the target relevance and the complex scene semantic understanding ability are enhanced, meanwhile, the calculation overhead is greatly reduced through rapid approximate matching and a hierarchical retrieval strategy, and the requirements of security application for high accuracy, rapid response and real-time intelligent research and judgment are met.

Proposed is a digital system using Locality Sensitive Hashing (LSH) for near duplicate detection on weighted datasets providing a robust technical solution to the problem by improving (i) efficiency by allowing for fast and accurate identification of near duplicates in large datasets; (ii) accuracy by extending the hashes calculated via 5 the LSH approach with an insurance-specific weight, thus making sure that the concept of similarity between documents is calculated on a technical and industry-relevant basis; (iii) cost reduction by reducing redundant data lowers storage and processing costs; (iv) data integrity by improved accuracy and consistency in records enhance overall data integrity; and (v) regulatory compliance matching by automated data 10 management practices supporting compliance with industry regulations.

The application relates to a file tree matching approximate retrieval method and system based on a local sensitive hashing algorithm, which comprises the following steps: calculating the sha1 values of sub-file trees and sub-files under a file tree through a local sensitive hashing algorithm, and generating a hash vector of the file tree; judging the similarity between file trees by calculating the distance between the hash vectors of two file trees; performing multiple clustering on the hash values of the file trees through a hierarchical clustering algorithm; in the search, starting from the clustering center point of the highest layer, comparing to obtain the nearest point, then searching the nearest child node under the point, and traversing all nodes in the lowest layer to return the top-k most similar file tree nodes. According to the application, a user inputs a file tree path, the program is parsed, the local sensitive hash value of the file tree is calculated while the traditional hash signature is generated, then the hierarchical clustering algorithm is used to perform multiple-layer clustering on the local sensitive hash value, and in the retrieval, the similar open source component library is quickly obtained through the clustering center nodes of each layer.

A data item identification service may use locality sensitive hashing to identify data items which are relevant to a search based on a hash of a search vector based on the search. Hashes of vectors based on data items may be stored using a set of bitmaps, where a given bitmap corresponds to a given bit position of a hash. Data items with matching or similar hashes may be related to each other. The data item identification service may perform searches by generating a hash for a search vector and comparing the hash to hashes for data items using one or more logical operations on the set of bitmaps to identify related data items.

The invention relates to the technical field of optical processing energy allocation, and discloses a point cloud topology perception-based fine structure self-adaptive polishing method, which comprises the following steps of: constructing an edge region geometric topology curvature tensor field by using in-situ white light interference point cloud data, and extracting a feature vector with geometric invariance; through a locality sensitive hashing algorithm, matching an energy allocation parameter containing the amplitude of the output current of the power supply adjusting circuit in the retrieval matrix, determining the power load pulse width required by the power supply adjusting circuit for driving the polishing load system, and controlling the polishing load system to operate along the Pierino fractal path, electric energy precise allocation is achieved through the power supply adjusting circuit, edge pressure nonlinear distribution is compensated, machining errors generated by boundary condition truncation are eliminated, materials are prevented from being excessively removed, coherence superposition of machining residual textures is damaged, and the conformal precision and the surface quality of the edge area of the precise optical assembly are effectively improved.

The invention discloses a short-term photovoltaic power prediction processing method, system and platform based on reversible normalization and day and night coding, and the method comprises the steps: obtaining historical meteorological data and historical photovoltaic power data of a photovoltaic station, and carrying out the data preprocessing; dividing a training set, a verification set and a test set according to a preset proportion; for each batch of examples in a training set, a verification set and a test set, a reversible instance normalization technology is introduced to effectively alleviate a data distribution offset problem, and a periodic rule of photovoltaic power generation is deeply fused by using refined day and night time coding. And the long sequence processing efficiency is improved by adopting an efficient attention mechanism based on locality sensitive hashing, so that the precision and the stability of short-term photovoltaic power prediction are remarkably improved.

This invention discloses an image retrieval method based on the Locality Sensitive Hash (LSH) algorithm, comprising the following steps: Step 1, acquiring multiple image data, preprocessing the multiple image data to obtain a densely represented image dataset A; using a d-dimensional vector x (x∈A) to represent one image data in the image dataset A, x=[x1,x2,...,x... i ,...x d Step 2: Based on image dataset A, initialize the SoftHash parameters and update them based on the image data; where the SoftHash parameters include: synaptic weight matrix W and neuron bias vector b; Step 3: Given an image s with dense representation, use the synaptic weight matrix W and neuron bias vector b obtained in Step 2 to generate a binary representation v of image s with hash length k; Step 4: Based on the obtained binary representation v of image s with hash length k, perform fast nearest neighbor retrieval using Hamming distance to find images with the same object instance as the query image.