28 results about "Similarity learning" patented technology

The invention relates to the field of off-post personnel identification, in particular to an off-post personnel identification system and method. The method is characterized in that a target detection module is constructed based on the OfficientViT, by means of the OfficientViT target detection module, by means of a linear attention mechanism and a multi-scale feature fusion technology, the high detection precision of 91.8% is maintained, efficient calculation processing is achieved, and in the trajectory tracking layer, by means of a quasi-dense similarity learning algorithm based on QDTrack and a cascade matching strategy, the detection precision of the target detection module is greatly improved. According to the method, the tracking capability of the system on a shielded target and a long-time disappearing target is greatly improved, in the behavior decision-making level, the space-time fusion decision-making module constructs an accurate departure behavior judgment mechanism by means of fusion time logic and space logic, and the overall departure recognition accuracy of the system is improved to 93.2%. A comprehensive experiment result shows that the system has excellent performance in various supervision scenes, and has relatively large performance advantages and practical value when being used for processing departure behavior identification in a complex environment.

The invention provides a foreign matter intelligent identification and dynamic monitoring method based on remote sensing data fusion, and belongs to the field of image identification. The problem of low foreign matter recognition efficiency is solved; the method specifically comprises the following steps: acquiring and extracting fusion features of remote sensing pictures of various road foreign matters; similarity learning is carried out on the fusion features, and a high-dimensional index structure is established; acquiring a real-time remote sensing picture on the road, and judging whether foreign matters exist on the road or not; if so, marking a foreign matter; if not, not processing; acquiring a historical natural image of a target area, designing a time sequence feature analysis module, a spatial feature analysis module and a spatio-temporal joint prediction module, and outputting a natural environment prediction image of the target area at a future moment; acquiring an actual remote sensing image, and marking an abnormal region in the actual remote sensing image according to the natural environment prediction image; according to the method, the foreign matter recognition efficiency is improved through obtaining and feature extraction of the remote sensing images in the road and natural environment of the target area.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image similarity retrieval method, device and equipment and a medium. Inputting the data and a feature pyramid into a similarity learning module for cross-level constraint training to obtain an updated module and a trained similarity learning module; and storing the training hierarchical semantic structure to a database management module, processing the query image by using the updated module to generate a query hierarchical semantic structure, performing multi-level comparison based on the trained similarity learning module to obtain a comprehensive matching result, and outputting a retrieval image set by the database management module. According to the method, unified modeling and alignment of multi-scale semantic features are realized through cross-level constraint training and multi-level comparison, and the retrieval accuracy and efficiency are improved.

An initial noisy graph topology is obtained and an initial adjacency matrix is generated by a similarity learning component using similarity learning and a similarity metric function. An updated adjacency matrix with node embeddings is produced from the initial adjacency matrix using a graph neural network (GNN). The node embeddings are fed back to revise the similarity learning component. The generating, producing, and feeding back operations are repeated for a plurality of iterations.

This invention discloses a remote sensing image crop classification method based on self-supervised learning. This method extracts rule-compliant crop sample data through a fully automated sample selection method. This crop sample data is processed into a coarse training sample set. Representation learning is performed using proxy tasks combined with an attention mechanism. Similar images of each remote sensing image are determined based on feature similarity. The proxy features are then used as prior conditions for semantic clustering, and refined classification is performed using the maximized dot product after softmax as the loss function. This method performs unsupervised training on the image's own feature similarity learning method, completing crop classification through clustering, avoiding the tedious and expensive process of manually annotating data. Compared with traditional manual feature extraction methods, this method is more adaptable to the complexity and variability of remote sensing images and can better utilize unlabeled data for training, improving the accuracy of crop classification.

The invention discloses an image-text cross-modal retrieval method and system based on high-dimensional ball embedding, and the method comprises the steps: carrying out the feature extraction processing of a target image and a target text through a backbone network and a word embedding method based on the target image and the target text, and obtaining a visual feature and a text feature; based on a ball encoder, performing center calculation and semantic uncertainty modeling of ball embedding to obtain a visual ball center vector, a visual uncertainty radius, a text ball center vector and a text uncertainty radius; similarity learning is carried out on the center vector and radius of the ball through a Monte Carlo sampling method of ball embedding, and image text cross-modal retrieval is achieved. According to the method, cross-modal alignment is enhanced through semantic uncertainty and diversity between visual texts, so that the cross-modal retrieval precision of the image texts is improved. The image-text cross-modal retrieval method and system based on high-dimensional ball embedding can be widely applied to the technical field of image-text retrieval.

The invention discloses a multi-view agricultural image clustering method based on enhanced multi-order similarity learning, and relates to the technical field of image clustering, and the method comprises the following steps: preprocessing image data, constructing an initial affinity matrix which is used for representing the affinity between data points, the method comprises the following steps: capturing a local structure and a neighborhood relationship of data points through first-order similarity and second-order similarity, stacking and rotating all affinity matrixes into a third-order tensor, constraining by using a weighted tensor Schatten-p norm, and optimizing all modules in a unified optimization framework. According to the method, through parallel mining of multi-order similarity, weighted tensor Schatten-p norm optimization and unified spectral clustering fusion, multi-view data complementary features are fully utilized, clustering precision and robustness are improved, and reliable technical support can be provided for precise agricultural application such as crop health monitoring and pest and disease damage detection.

The application discloses an industrial control abnormality detection method based on high-low frequency feature similarity, which firstly carries out periodic collection of monitoring data from an industrial control system field, and constructs corresponding low-frequency feature vectors and high-frequency feature sets. Through repeated data collection, a training data set containing multiple samples is formed. In the initialization process of the abnormality detection network, a combination of a feature transformation network and a projection network is adopted to ensure effective mapping and fusion of the low-frequency features and the high-frequency features. Specifically, the low-frequency feature vectors are mapped to a unified dimension through a linear transformation matrix, and the high-frequency features are directly input into an attention fusion mechanism to calculate the dynamic correlation degree of the high-frequency features to the low-frequency features. Finally, the projection network is used to map the fused features and the low-frequency features to generate a vector pair for similarity learning. The application can solve the technical problems of the deficiencies of the conventional industrial control system abnormality detection method in feature processing and fusion.

The invention provides an equipment energy consumption state monitoring method based on electric signals of a multi-energy complementary energy supply system, and the method comprises the steps: obtaining time sequence electric signals of all energy consumption equipment, carrying out the preprocessing, extracting multi-dimensional features, constructing feature vectors, and carrying out the collection to obtain an original sample set; disturbance enhancement is applied to the original sample in the known state, and an abnormal behavior agent sample set is generated; the method comprises the following steps: constructing an Encoder-Classifier architecture model on the basis of a two-stage model training mechanism; acquiring electric signals in real time, outputting the electric signals according to a fixed window, preprocessing and extracting features, and inputting the features into the model to obtain monitoring results. According to the method, the electric signal time sequence characteristic and the equipment operation rule are combined, the abnormal behavior agent sample can be automatically generated without an abnormal label, and a complex equipment scene is adapted; through double-stage training of data enhancement and similarity learning, the model recognizes the state and abnormity of equipment, and intelligent support is provided for safe operation and energy efficiency improvement of a multi-energy system.

Systems and methods for preference and similarity learning arc disclosed. The systems and methods improve efficiency for both searching datasets and embedding objects within the datasets. The systems and methods for preference embedding include identifying paired comparisons closest to a user's true preference point. The processes include removing obvious paired comparisons and / or ambiguous paired comparisons from subsequent queries The systems and methods for similarity learning include providing larger rank orderings of tuples to increase the context of the information in a dataset In each embodiment, the systems and methods can embed user responses in a Euclidean space such that distances between objects are indicative of user preference or similarity.

The application provides a kind of based on the energy state monitoring method of equipment of multi-energy complementary energy supply system electric signal, steps include: obtaining each energy equipment timing electric signal, extracts multi-dimensional feature after pre-processing and constructs feature vector, and obtains original sample set in set;Known state original sample is applied to enhance, and abnormal behavior agent sample set is generated;Based on the two-stage model training mechanism, the Encoder-Classifier architecture model is constructed;Real-time acquisition of electric signal and output according to fixed window, pre-treatment is extracted into the model after feature and the monitoring result is obtained.The application combines the timing characteristics of electric signal and the operation law of equipment, can automatically generate abnormal behavior agent sample and does not need abnormal label, adapts to complex equipment scene;Through two-stage training of data enhancement and similarity learning, the model identifies equipment state and abnormality, and provides intelligent support for safe operation and energy efficiency improvement of multi-energy system.

The application discloses an image similarity matching method based on attention weight distribution of external memory, comprising the following steps: 1, obtaining image original features of an image data set; 2, replacing the image original features with the result of representation learning to obtain high-level representation thereof; 3, performing simple similarity calculation on input samples to obtain external initial memory thereof; 4, constructing two external selection models to distribute external memory selection weights, including an external sample selection model and an external relationship selection model; 5, optimizing and training the external selection models by using two training methods of selection optimization and global optimization to obtain an optimal training model; and 6, detecting the input samples by using the trained external selection model to obtain an image pair with the highest external memory selection weight as the most similar image pair. The application can improve the similarity learning effect, thereby obtaining the globally most similar image, and the image matching result has better interpretability.

The application discloses a long video retrieval method and device based on multi-scale multi-example similarity learning. The method acquires video and text preliminary features; uses coarse-to-fine coding mode to extract information of different time granularities from video segment scale and frame scale; based on video representation of two scales, uses segment scale similarity learning branch to filter out video segments most relevant to the text and obtain segment scale similarity; uses frame scale similarity learning branch to aggregate video features guided by the filtered most relevant video segments to obtain more detailed video information, and after similarity calculation with the text, frame scale similarity is obtained; a common space learning algorithm is used to learn multi-scale similarity between long videos and texts, and a model is trained in an end-to-end manner to realize text-to-long video retrieval. The application uses the idea of multi-scale multi-example learning, and can effectively solve the text-to-long video retrieval task.

Systems and methods for similarity learning in digital pathology are provided. In one aspect, an apparatus for generating training image data includes a hardware memory configured to store executable instructions and a hardware processor in communication with the hardware memory, wherein the executable instructions, when executed by the processor, cause the processor to obtain a plurality of histopathology images, classify two or more of the histopathology images as similar or dissimilar, and create a dataset of training image data including the classified histopathology images.

The invention discloses an industrial control anomaly detection method based on high and low frequency feature similarity, and the method comprises the steps: carrying out the periodic collection of monitoring data from an industrial control system site, and constructing a corresponding low-frequency feature vector and a high-frequency feature set; through repeated data acquisition, a training data set containing multiple samples is formed. In the initialization process of the anomaly detection network, the combination of the feature transformation network and the projection network is adopted to ensure the effective mapping and fusion of the low-frequency features and the high-frequency features. Specifically, low-frequency feature vectors are subjected to unified-dimension mapping through a linear transformation matrix, and high-frequency features are directly input into an attention fusion mechanism so as to calculate the dynamic correlation degree of the high-frequency features to the low-frequency features. And finally, mapping the fusion feature and the low-frequency feature by using a projection network, and generating a vector pair for similarity learning. The technical problem that a traditional industrial control system anomaly detection method is insufficient in feature processing and fusion can be solved.

The invention discloses an image-text retrieval method and system based on fine-grained alignment and reordering, and the method comprises the steps: inputting an image and a text to be retrieved, and carrying out the coding of the inputted image-text through the powerful feature extraction capability of a pre-training model CLIP; adaptively aligning the text representation to a related image region by using a cross-modal interaction module; calculating a similarity score between the image and the text to obtain a preliminary matching result; performing reverse retrieval on the initial similarity matrix through a reordering mechanism; a retrieval model is trained in combination with three loss functions, knowledge extracted offline by a single-mode pre-training teacher model is introduced as a soft label supervision signal, and a similarity learning process is optimized; the KL divergence is used to measure the difference between the probability distribution output by the model and the soft label provided by the teacher model, and the semantic alignment capability between images and texts is improved. According to the method, three loss models are used in a combined manner, so that the semantic relationship between images and texts is effectively aligned, and the clearness and consistency of the internal structure of the modal are ensured.

The present application relates to the field of trajectory recognition, and particularly relates to a kind of open environment vehicle abnormal trajectory detection model architecture method, its model includes coding network, embedding network and inference network.In training phase, first, the total loss including similarity loss and cross-entropy loss is used to update model parameters, then, the updated model is used to update similarity learning network using cross-entropy loss until training is completed.The calculation of the similarity loss includes: based on trajectory embedding, the similarity between trajectories is calculated using similarity learning network, the cross-entropy loss is calculated based on the predicted probability distribution and the similarity between trajectories, then, the similarity loss is calculated by aligning the two types of similarity.The method of the present application introduces similarity loss, so that the model not only learns the classification of a single trajectory, but also understands the relationship and connection between different trajectories, which can effectively classify unknown abnormal behavior in the environment under the assumption of open world.

The disclosure provides a context-guided multimodal association-based image-text retrieval method and system, and relates to the technical field of cross-modal image-text mutual retrieval, which comprises the following steps: acquiring fine-grained feature sequences of different modal images and text data; constructing a context-guided multimodal association learning network to respectively acquire regionally spatially enhanced visual context perception representations of image modal and word temporally enhanced text context perception representations of text modal; constructing a context-guided multimodal association three-branch to perform cross-modal similarity learning on context perception representations of different semantic levels, learning cross-modal association by using a vector-type similarity function, designing an objective function, and respectively realizing complementation of different semantic levels within a mode and semantic accurate alignment between different modal data based on a joint learning semantic consistency loss function and a cross-modal matching loss function; and the disclosure can realize accurate alignment between different modal.

Preference and similarity learning systems and methods that improve efficiency for both searching datasets and embedding objects within the datasets. The systems and methods for preference embedding include identifying paired comparisons closest to a user's true preference point. The processes include removing obvious paired comparisons and / or ambiguous paired comparisons from subsequent queries. The systems and methods for similarity learning include providing larger rank orderings of tuples to increase the context of the information in a dataset. In each embodiment, the systems and methods can embed user responses in a Euclidean space such that distances between objects are indicative of user preference or similarity.

The invention discloses a multi-view agricultural image clustering method based on multi-order bipartite graph learning, and relates to the technical field of image clustering, and the method comprises the steps: learning a direct relation between a data point and an anchor point through a first-order similarity learning module, thereby optimizing an initial bipartite graph matrix, extracting local features of a multi-view agricultural image, and carrying out the clustering of the multi-view agricultural image. The model is a first-order similarity learning model. A reconstruction error is constructed through a second-order similarity learning module, neighborhood structure features between data points and anchor points are captured to optimize an initial bipartite graph matrix, and the initial bipartite graph matrix is combined with a first-order similarity learning module to construct a second-order similarity learning model. A tensor is constructed through an enhanced third-order similarity learning module, a tensor Schatten-p norm and anchor point structure regularization are combined, high-order correlation of an initial bipartite graph between different visual angles is mined, the relation between anchor points is considered, a first-order similarity learning module and a second-order similarity learning module are integrated, and therefore an enhanced multi-order similarity learning model is constructed. The optimization model generates a high quality global representation.

The application discloses a tobacco slitting method based on multi-view weight learning and a storage medium, obtains a hyperspectral image of target batch tobacco and performs division; an effective area of the tobacco is obtained by using a threshold segmentation method, the tobacco is divided into a set number of subareas with the same longitudinal length, characteristic spectra of the subareas corresponding to the tobacco are calculated, and a tobacco spectrum database is constructed; spectrum data in the tobacco spectrum database is subjected to band division, different band combinations are obtained, and a multi-view tobacco spectrum database is constructed; a semi-supervised clustering algorithm model is constructed based on multi-view weight and similarity learning, the semi-supervised clustering algorithm model is trained through the multi-view tobacco spectrum database, and a semi-supervised clustering algorithm model with an optimized target function is obtained; and a tobacco segmentation result is obtained through the semi-supervised clustering algorithm model. Through construction of the multi-view of the tobacco, weights are allocated to each view, differences between different tobaccos are accurately quantified, and the tobacco slitting effect is ensured.

The invention relates to the technical field of medical care informatics, and discloses a method and a system for constructing a fetal heart monitoring auxiliary interpretation model based on similarity learning, and the method comprises the steps: training a model to learn a relationship between a fetal heart monitoring image and fetal health condition score data by using a twin network in combination with a similarity algorithm; the twin network can efficiently process and compare fetal heart monitoring images through a weight sharing mechanism, multiple models do not need to be repeatedly trained, the efficiency is remarkably improved, and compared with a traditional machine learning classification task, the method has the advantages that the classification efficiency is improved, and the classification efficiency is improved. The similarity matching task can more meticulously capture subtle differences between the fetal heart monitoring images, especially tiny health changes (such as fluctuation of the fetal heart rate or changes of uterine contraction) in the fetal heart monitoring images, can more accurately predict the fetal health condition score, is helpful for realizing more accurate and efficient fetal heart monitoring, and improves the fetal heart monitoring efficiency. And doctors are helped to make more efficient and accurate medical decisions for pregnant women and fetuses.

The present application belongs to the technical field of image anomaly detection, and discloses an image anomaly detection method based on single-expert dual-apprentice, which comprises an expert network, two apprentice networks, and is divided into two stages of training and testing. In the training stage, the two apprentice networks are subjected to similarity learning using unknown abnormal images and real known abnormal images respectively, so that the normal features between the two apprentice networks and the expert network have high similarity, and the features of real known and unknown abnormal data have low similarity. In the testing stage, the three networks are used respectively to extract features of the test images, the similarity between the two apprentice networks and the expert network is calculated, and the negative value is taken as the abnormal score. Then, the abnormal scores generated by the two apprentice networks are added to obtain the final abnormal score. The method effectively utilizes the small amount of abnormal information in industrial products and improves the performance of automatic quality inspection.

The application discloses a kind of automatic driving vehicle multi-target tracking algorithm based on re-identification and motion model, target detection module obtains target position, confidence and the like information by detection network.Information is transported to backbone network by slice operation to detection image by re-identification module through image slice module, obtains each slice image feature, and then appearance feature is strengthened by similarity learning self-attention module.Initialization module is sent into subsequent trajectory matching association module by being divided into high score detection frame and low score detection frame according to the threshold value set by obtaining the detection frame information of target detection system by tracker.Motion prediction module uses Kalman filter to predict next frame target boundary frame.Trajectory matching association module calculates the cost matrix of target detection frame and trajectory by combining intersection over union and appearance cosine distance of adaptive weighting.The application improves the tracking effect of pedestrian multi-target tracking algorithm on automatic driving vehicle.

The present invention discloses an image similarity matching method based on a hierarchical structure of multi-scale and multi-modal features, comprising the following steps: 1. obtaining original multi-modal features of image samples; 2. performing multi-scale transformations on the multi-modal features and fusing them into comprehensive features; 3. performing hierarchical clustering on the samples to obtain the hierarchical structure of the samples, and determining the number of neurons and the connection mode of each layer; 4. constructing a similarity equation based on the comprehensive features and the hierarchical structure; 5. constructing a target equation, training the model using a global optimization method, iteratively updating each relational neuron, and obtaining an optimal training model; 6. using the trained model to perform image similarity matching on input samples, obtaining the image pair with the highest similarity as the most similar image pair. The present invention can better learn the comprehensive features of images, thereby improving the effect of image similarity learning and making the image matching results more interpretable.

The application discloses a multi-view agricultural image clustering method based on multi-order bipartite graph learning, relates to the technical field of image clustering, and comprises the following steps: learning the direct relationship between data points and anchor points by a first-order similarity learning module to optimize an initial bipartite graph matrix, and extracting local features of multi-view agricultural images, wherein the model is a first-order similarity learning model; constructing a reconstruction error by a second-order similarity learning module, capturing the neighborhood structure features between the data points and the anchor points to optimize the initial bipartite graph matrix, and constructing a second-order similarity learning model in combination with the first-order similarity learning module; constructing a tensor by an enhanced third-order similarity learning module, mining the high-order correlation of the initial bipartite graph between different views and considering the relationship between the anchor points and the anchor points in combination with a tensor Schatten-p norm and anchor point structure regularization; integrating the first-order similarity learning module and the second-order similarity learning module to construct an enhanced multi-order similarity learning model, and optimizing the model to generate high-quality global representation.

The invention relates to the technical field of data analysis in image processing. The invention provides a multi-view face data clustering method based on low-dimensional kernel domain difference measure, and the method comprises the steps: carrying out the noise reduction and redundancy elimination processing of face feature data of each view in multiple views, and generating a low-noise low-redundancy feature matrix after the dimension reduction; on the basis of the low-dimensional feature matrix, similarity measurement normal form reconstruction and optimization processing between the features are carried out, and an optimized distance measurement matrix is generated; according to the optimized distance measurement matrix, sample similarity learning is carried out, and an initial similarity matrix is generated; according to the method, the initial similarity matrix of each view angle is subjected to weighted fusion to form a consistency similar matrix, and finally, the consistency similar matrix is utilized to perform spectral clustering to generate a clustering result, so that the balance between the calculation efficiency and the clustering precision is improved, the adaptability of high-redundancy data is enhanced, and compared with other methods, the accuracy of the method is higher.