18 results about "Hamming space" patented technology

The invention provides a remote sensing image retrieval optimization method based on automatic weight distribution and comparative Hash, a comparative Hash network model based on automatic weight distribution is constructed, and the performance of the model is optimized by adopting a double-stage training strategy. In the feature learning stage, automatic weighting comparison loss is provided, Gaussian weighting and a dynamic adjustment strategy are introduced to improve a traditional loss function, different weight values are given according to the importance between sample pairs, and learning of the model on key sample pairs is enhanced; a threshold value is set to identify the difficult-to-negative sample, so that the model is not interfered by the difficult-to-negative sample. In the Hash learning stage, a Hash layer is added to convert high-dimensional image representation into binary Hash codes, and quantization loss is introduced to learn the spliced and fused Hash codes, so that a semantic similar structure among data in a Hamming space can be kept, and meanwhile, the identification of the Hash codes is improved by utilizing automatic weighting comparison loss. According to the method, the target remote sensing image can be effectively retrieved, and the retrieval speed is relatively high.

The invention discloses a micromedical cross-modal Hash retrieval method based on lifelong learning, and the method comprises the steps: carrying out the coding of basic medical data through a double-tower Transform, and inputting the coded features into a Hash learning module; then, a semantic alignment module is used for constraining distance and label correlation in and across modals in the medical data, so that multi-modal data with the same or different semantics are close to or farther than one another when mapped to a Hamming space, and when incremental medical data of a new category arrives, a lifelong Hash learning module is designed; incremental data is directly mapped into a hash code instead of training a hash function by using accumulated medical data, so that catastrophic forgetting of hash learning is effectively avoided. According to the method, the training time and computing resources are saved from a medical stream scene, and continuous and efficient retrieval of clinical stream data is realized.

The invention discloses a self-paced Hash multi-modal learning method for noise tags. The method comprises the following steps: S1, obtaining feature data sets and tag data sets of all modals; s2, initializing a hash center for each category; s3, each modal feature is projected to a public Hamming space by using a modal-specific sub-network, the loss of each sample is calculated, noise samples are distinguished by using a self-paced function, the loss of other samples is weighted, and finally all sub-networks and hash centers are optimized; s4, repeating the step S3 to optimize all the sub-networks and the Hash centers until convergence; s5, inputting a test set sample of each mode, and obtaining a hash code by using a sub-network specific to the mode; according to the method, the noise instance can be identified by evaluating the learning difficulty of each instance, and each instance in the Hamming space can be learned from easy to difficult, so that the model has higher robustness and generalization ability.

The present invention discloses an image hash retrieval method based on center mapping. The method converts images into hash codes for retrieval, reduces storage and computational requirements, and improves retrieval efficiency. A deep model is used to map images into hash codes, which can better extract image features and retain more semantic information with a relatively small number of bits. The quantized hash codes are used as learning targets, making the errors caused by the final quantization negligible and improving the quality of the hash codes. A low-dimensional learning target is constructed using feature center mapping, so that the similarity information of images in high-dimensional feature space can be relatively completely retained in low-dimensional Hamming space, resulting in more accurate retrieval results. The model is trained in an unsupervised manner, greatly reducing the data annotation cost and expanding the scope of application of the method. The above method has been successfully applied in searching for decoration images, and can more quickly and intuitively determine the degree of decoration of a decoration image.

The application discloses a cross-modal hash retrieval method and relates to the technical field of cross-modal learning. The method comprises the following steps: 1) a cross-modal feature learning network comprising an image network, a text network and a label network is used to map each triple information to a high-dimensional Hamming space through a feature fusion graph attention classification learning module; 2) a SwinT-S (SwinT Small model) model is used to extract semantic features of images; 3) a graph attention feature fusion attention module is used to perform deep fusion alignment on the obtained image semantic features and text semantic features; 4) a deep text feature extraction network is used to optimize the generation of text hash codes and generate high-quality text hash codes; and 5) a linear layer (Linear) and a Tanh (t) function are used to map text features into hash code lengths required by the application. The application has the beneficial effect of effectively reducing semantic gap problems, fusing different modal features and improving the performance of cross-modal hash retrieval.

The application discloses a remote sensing video hash retrieval method based on key frame fusion and an attention mechanism, and mainly solves the problems that existing methods cannot fully capture the category-level semantics of remote sensing videos and the semantic information is insufficiently utilized. A new video space-time information extraction structure is designed to extract the data representation of three-dimensional video data in two-dimensional space, and an attention mechanism is introduced under the structure of a convolutional neural network to extract the corresponding hash code of the video in the Hamming space. The application not only captures the relative semantic correlation of hash codes in different modes, learns the relative semantic correlation of deep features, but also enhances the category-level semantics of hash codes and reduces the quantization error between class hash codes and hash codes. The application fully utilizes the space-time semantic information of remote sensing videos, and further improves the retrieval performance.

The invention belongs to the field of multi-modal information retrieval, and discloses a deep semantic guided unsupervised multi-modal Hash retrieval method, which is characterized in that a teacher model adopts powerful modal interaction information provided by a VLP model, a similar matrix and a graph convolutional neural network are constructed, and the semantic gap between a real value space and a Hamming space is made up; rich semantic information in the teacher model is transmitted to the student model in a knowledge distillation mode, and the student model is optimized, so that efficient learning of the small model is promoted; the student model inherits the advantages of the teacher model and avoids hyper-parameter adjustment pressure of a large-scale model, so that the method is more suitable for a large-scale data scene in practical application. A Hamming distance optimization strategy is designed, it is ensured that the complex semantic relation in the real value space is reasonably quantified and expressed in the Hamming space, and excessive compression from the real value space to the Hamming space is avoided.

The invention discloses an ECG hash coding method and system based on sample similarity guidance, and belongs to the technical field of biological signal data machine learning, and the method comprises the steps: obtaining a data set containing a plurality of ECG signals, extracting features, and constructing an ECG feature matrix; for unsupervised learning, a feature space measurement matrix and a Hash space modulation matrix are introduced, and a generalized similarity preserving equation for aligning an Euclidean space and a Hamming space is constructed in combination with an ECG feature matrix and a to-be-solved Hash coding matrix; for supervised learning, introducing a semantic space measurement matrix on the basis of unsupervised learning, and constructing generalized similarity preserving equations which simultaneously align the Euclidean space and the Hamming space and align the semantic space and the Hamming space; converting the equation into a matrix optimization problem, and solving to obtain a Hash coding matrix; and the Hash coding matrix is used for coding the ECG signal, so that the Hash coding quality and the auxiliary diagnosis retrieval efficiency can be effectively improved.

The present invention discloses a feature matching method based on a multi-level refinement strategy, belonging to the technical field of computer vision. The present invention generates an initial correspondence by using the similarity of the local appearance of the feature descriptor in the Hamming space, combines the local image motion smoothness constraint, and uses the GMS algorithm to improve the accuracy of the initial matching; finally, the PROSAC algorithm is used to optimize the matching to obtain an accurate matching based on the global gray information in the Euclidean space. The experimental results show that the feature matching method based on the multi-level fine matching strategy of the present invention reduces the error by an average of 29.92% compared with the ORB algorithm in complex scenes with illumination changes and blurring, effectively improving the matching accuracy of feature matching in complex spaces.

The invention discloses a Hash retrieval method based on spatial crossover shift and Fourier frequency domain learning, and mainly relates to the field of deep Hash image retrieval. Comprising the following steps: S1, respectively and correspondingly dividing an image data set and category label information corresponding to the image data set into a training set, a query set and a database; s2, preprocessing the image information; s3, constructing a Transform deep hash retrieval architecture based on spatial cross shift and Fourier frequency domain learning; s4, starting to train the images in batches in turn; s5, randomly inputting a query set, generating query Hash codes through a network, calculating Hamming space distances from the query Hash codes to database Hash codes, sorting the distances from small to large, and finding corresponding image groups according to previous several returned Hash code results; according to the method, the high-level semantic expression of the deep features can be obtained, interactive learning between different domains can be promoted, the semantic information accuracy can be improved, and the robustness of the model can be improved.

The present invention discloses a large-scale short video retrieval method, system and device based on deep learning. The method of the present invention first performs key frame extraction and video standardization processing on the short video to be retrieved; then inputs the processed short video into a short video semantic feature extraction network, and uses similarity calculation to obtain T similar videos, where T is a preset value; the present invention designs a new short video semantic feature extraction network to extract the data representation of three-dimensional video data in a two-dimensional space. And a hash representation layer is introduced under the structure of the inherent convolutional neural network to obtain the corresponding hash code of the short video in the Hamming space. The present invention not only captures the relative semantic correlation of hash codes in different modes, but also significantly reduces the spatio-temporal complexity of short video retrieval. The present invention makes full use of video spatio-temporal semantic information to further improve the retrieval performance.

The application provides a remote sensing image retrieval optimization method based on automatic weight distribution and contrastive hashing, constructs a contrastive hashing network model based on automatic weight distribution, and adopts a two-stage training strategy to optimize the model performance. In the feature learning stage, an automatic weighted contrastive loss is proposed, a Gaussian weighting and dynamic adjustment strategy is introduced to improve the traditional loss function, different weight values are given according to the importance of sample pairs, and the learning of the model on key sample pairs is strengthened; a threshold is set to identify difficult negative samples, so that the model is not disturbed by the difficult negative samples. In the hash learning stage, the hash layer is added to convert the high-dimensional image representation into binary hash code, the quantization loss is introduced to learn the hash code after splicing and fusion, so that the semantic similar structure between data in the Hamming space can be kept, and the automatic weighted contrastive loss is used to improve the identification of the hash code. The application can effectively retrieve the target remote sensing image, and the retrieval speed is relatively fast.

The invention discloses a streaming modal data-oriented cross-modal hash retrieval method. The method specifically comprises the following steps of: obtaining a feature data set and a tag data set of a modal; a knowledge base is constructed for each mode and used for storing semantic information of each mode; projecting modal features to a public Hamming space by using the sub-network of each modal, performing knowledge base mining, hash code alignment and knowledge base migration, and optimizing the sub-networks and the knowledge base; and repeatedly optimizing the sub-network and the knowledge base until convergence. The streaming media Hash learning normal form is utilized, the Hash code of each mode can be learned in parallel to process the streaming media data, all historical modes do not need to be trained again, and therefore the training complexity is reduced; the Hash technology is adopted to map high-dimensional features into compact binary Hash codes, the storage requirement is greatly reduced, the similarity of the Hash codes is measured through simple bitwise XOR operation, and the calculation speed is greatly increased.

The application provides a cross-modal retrieval method and device based on an attention network and an anti-hash, and the method comprises the following steps: obtaining image-text pair data, extracting initial global features of image data and text data, processing the initial global features of the image data and the initial global features of the text data through a shared attention module respectively to obtain final feature representations of the image data and the text data; a modal discriminator interacts with a feature extractor in an anti-hash manner to promote the modal feature extractor to learn better image-text features; the final feature representations of the image data and the text data are converted into binary codes, the features are mapped into a common Hamming space, cross-modal retrieval is performed, and the first K cross-modal retrieval results are obtained. The application combines an anti-hash network with a hash learning network, utilizes the anti-hash network learning to promote the feature extractor to learn modal invariant representation, simultaneously utilizes the characteristics of hash fast retrieval, and thus cross-modal retrieval is realized.

The application relates to the technical field of data retrieval, and discloses a cross-modal hash retrieval method and system with consistency, which comprises the following steps: S1. acquiring heterogeneous data; S2. acquiring hash codes of the heterogeneous data, and performing hash code learning on the heterogeneous data; S3. performing hash function learning according to the obtained optimal hash code; and S4. mapping the heterogeneous data to the same low-rank Hamming space through the hash function after the function learning is completed, using an exclusive OR operation to the similarity of the heterogeneous data and a retrieval set, returning a result with high similarity, and completing cross-modal retrieval of the heterogeneous data to be retrieved. The application solves the problems of insufficient retrieval precision and complicated optimization process in the prior art, and has the characteristics of being capable of overcoming the heterogeneity of cross modalities.

The application belongs to the technical field of information security and data retrieval, and discloses a low-misjudgment cross-modal ciphertext retrieval method based on subset predicate encryption, wherein a data owner maps data of different modes to the same Hamming space to generate binary hash codes; then, the binary hash codes are subjected to bucketing to establish an inverted index of the hash bucket number to the encrypted object identification list; then, a symmetric subset predicate encryption is used to encrypt an XOR filter to generate a lightweight encrypted index; when querying, a client generates a hash bucket number pair set to construct a search token and send it to a server once; after subset verification in the encrypted XOR filter, the server returns encrypted multi-modal data in the corresponding bucket. The application uses an XOR filter to eliminate the false positive misjudgment of a traditional Bloom filter, significantly improves retrieval accuracy, reduces server-side retrieval complexity to a constant level, and realizes low-misjudgment, high-efficiency and low-overhead cross-modal ciphertext retrieval under the premise of ensuring privacy.

The present invention belongs to the field of multimedia retrieval technology, and provides a video clip positioning method, system, medium and device for real-time tasks. The technical scheme is as follows: after obtaining the video and query statement, the corresponding feature representation is obtained through the image feature processing model and the natural language feature processing model; the feature representations of two different modalities, video and query statement, are mapped to the same Hamming space through cross-modal dynamic hash coding, and the dynamic hash can adjust the hash coding according to the data input; the knowledge distillation technology is used to extract knowledge from a teacher model with large parameter volume and high performance, and migrate it to a student model with small parameter volume and low complexity, so as to construct a lightweight video clip positioning model. The present invention constructs a lightweight model by integrating dynamic hash coding technology and distillation learning technology, which realizes efficient capture of the deep semantics of the video content, while ensuring the rapid response and accuracy of real-time tasks, and solves the difficulty of video clip positioning.

A system and method for compaction of floating-point numbers within a dataset, comprising a pre-encoder, a data deconstruction engine, a library manager, a codeword storage, and a data reconstruction engine. A pre-encoder may receive a plurality of data sourcepackets with may contain one or more floating-point numbers and the received data sourcepackets are scanned to identify floating-point numbers and the identified floating-point numbers. Identified floating-point numbers may be pre-encoded into binary string representations which are low-distortion embeddings of real numbers into a Hamming space. The binary string representation may be indexed to indicate it represents a floating-point number before being compacted by a data deconstruction engine and library manager. The pre-encoding of floating-point numbers located within a sourcepacket enables the system to maximize the benefit of the compaction capabilities of the data deconstruction engine.