80results about How to "Bridging the "Semantic Gap"" patented technology

The present invention provides a method for performing transactions on data entities in a database and a transactional database. The database comprises an ordered set of data stores with at least one static data store, wherein said static data store uses an index structure based on a non-updatable representation of an ordered set of integers according to the principle of compressed inverted indices. The method allows to generate a modifiable data store when the performed transaction comprises an insert, update or delete operation, to execute operations of the transaction on the ordered set being present at the time when the transaction has been started and, if present, on the modifiable data store and to convert data stores to a new static data store, The insert, update or delete operation are executed on the modifiable data store which is the only data store modifiable for the transaction.

The invention discloses a correlation-quality sequencing image retrieval method based on tag retrieval. The correlation-quality sequencing image retrieval method comprises the following steps of: firstly, automatically sequencing social images according to the correlation of images and tags, fusing the vision consistency among the images and the vision consistency of the semanteme correlation among the images and the tags by utilizing a normalization frame, and solving an optimization problem by utilizing an iteration algorithm so as to obtain a correlation sequence; and selecting the brightness, the contrast ratio and the color variety of the images to be used as quality characteristics of the images to balance previously returned images, overlaying correlation scores and quality scores by utilizing a linear model, and re-sequencing the images according to a final total score, and therefore, a correlation-quality sequencing algorithm is realized.

The invention is applicable to the technical field of image processing and provides a semantic mapping method of the local invariant feature of an image. The semantic mapping method comprises the following steps of step A: extracting and describing the local invariant feature of the colorful image; step B: after extracting the local invariant feature, generating a visual dictionary for the local invariant feature extracted from the colorful image on the basis of an algorithm for supervising fuzzy spectral clustering, wherein the visual dictionary comprises the attached relation of visual features and visual words; step C: carrying out semantic mapping and image description on the attached image with the local invariant feature extracted in the step A according to the visual dictionary generated in the step B. The semantic mapping method provided by the invention has the advantages that the problem of semantic gaps can be eliminated, the accuracy of image classification, image search and target recognition is improved and the development of the theory and the method of machine vision can be promoted.

The invention discloses an image retrieval method based on semantic mapping space construction. The image retrieval method is characterized by comprising the steps of (1) learning semantic mapping space, (2) estimating a semantic concept of each image which is not labeled, (3) conducting ascending sorting on the images corresponding to the semantic concepts in the semantic space, (4) inputting text search terms to be retrieved, and returning the images corresponding to the semantic concepts. According to the image retrieval method, the image retrieval accuracy can be effectively improved.

The invention discloses a data integration method based on semantic heterogeneous and isomerous relational database. It achieves the sharing of massive relational data through construction of ontological warehouse in the field and register various relational database to body model. The invention effectively shield model differences exist in the relational database of different medium and structure, compared with data integration method of other heterogeneous and isomerous relational database, this invention embodies good performances in feasibility, dynamic augmentability, universality and transparency aspect.

The invention discloses an antagonistic cross-media retrieval method based on bilingual meaning space, which relates to the technical fields of pattern recognition, natural language processing, multimedia retrieval and the like. Including: feature generation process, bilingual semantic space construction process and antagonistic semantic space optimization process. The invention realizes that theoriginal image and the text information are kept to the maximum extent while the semantic gap is eliminated by establishing an isomorphic bilingual meaning space, namely a text subspace and an image subspace. And through antagonism training to optimize the distribution of isomorphic subspace data, mining the rich semantic information in multimedia data, and fitting the vector distribution of different modes in the semantic space under the condition that the categories remain unchanged and the modes can be distinguished. The method of the invention can effectively eliminate the heterogeneity ofdifferent modal information, realize effective cross-media retrieval, and has wide market demand and application prospect in the fields of picture and text retrieval, pattern recognition and the like.

The invention discloses a high spectral image classification method based on morphological characteristics and dictionary learning. The method comprises the following steps of extracting morphological characteristics from high spectral images, a process of dictionary learning, coding the characteristics, and classifying the images. The method is applied to the field of high spectral image classification, the structural relation of space information in the high spectral images is taken into full consideration, high-level semantic mapping is constructed on the basis of the space relation information, high-level semantic codes which can maintain the structure information of the characteristic space effectively are obtained and used for a high spectral image classification task, and the problem that semantic gaps exist between the high level meanings and bottom characteristics of the high spectral images is overcome. The method has substantial effect in high spectral image classification, and has higher application values.

The invention provides a cross-media information analysis and retrieval method, which comprises the following steps of: performing semantic integration processing on multimode information; performing expansion according to a probability latent semantic analysis model to obtain a multilayer continuous probability latent semantic analysis model for processing a continuous feature vector; learning the multilayer-continuous probability latent semantic analysis model by adopting an asymmetric learning method, and calculating the visual feature vector distribution of an image, the visual feature vector distribution of an audio and topic probability distribution; submitting a training set and a tested media object which serves as a retrieval example by a user, and calculating intra-mode and inter-mode initial similarity values of the image and the audio in the retrieval sample; constructing a propagation model, and updating the intra-mode and inter-mode similarity values according to the propagation model; and performing secondary retrieval according to the updated similarity values.

The invention belongs to the field of image processing, and particularly relates to a CT image segmentation method based on an improved AU-Net network, and the method comprises the steps: obtaining a to-be-segmented brain CT image, and carrying out the preprocessing of the obtained brain CT image; inputting the processed image into the trained improved AU-Net network for image recognition and segmentation to obtain a segmented CT image; identifying a cerebral hemorrhage area according to the segmented brain CT image. The improved AU-Net network comprises an encoder, a decoder and a hopping connection part. Aiming at the problem of low segmentation precision caused by large size and shape difference of the hemorrhage part of the cerebral hemorrhage CT image, the invention provides a coding-decoding-based structure, and a residual octave convolution module is designed in the structure, so that the model can segment and identify the image more accurately.

The invention discloses an image description method based on self-adaptive local concept embedding, and belongs to the technical field of artificial intelligence. The method comprises the following steps: 1, extracting a plurality of candidate regions of an image to be described and features corresponding to the candidate regions by adopting a target detector; 2, inputting the features extracted in the step 1 into the trained neural network so as to output a description result of the to-be-described image. Aiming at the defect that the traditional attention mechanism-based image description method has no explicit modeling of the relationship between the local area and the concept, the invention provides a scheme for adaptively generating the visual area through a context mechanism and generating the visual concept thereby, and enhances the connection from vision to language, thereby improving the accuracy of generated description.

The invention discloses an image sentiment analysis method based on a multi-task learning mode. The method comprises the following steps: constructing an image sentiment attribute detector and an image sentiment label classifier; using a gradient descent method to train initialization parameters of the image emotion attribute detector; testing the prediction precision of the emotion attributes of the image and judging whether the emotion attributes reach the standard or not, if yes, reasonably designing the training parameters of the detector, otherwise, retraining; taking the output of the image emotion detector and the convolution characteristics of the original image as the input of an emotion label classifier, and training classifier initialization parameters by adopting a gradient descent method; testing the prediction precision of the label classifier and judging whether the prediction precision reaches the standard or not, namely, reasonably designing training parameters of the label classifier when the prediction precision reaches the standard, otherwise, retraining; classifying the image emotion tags, and analyzing the image emotion. According to the method, the influence caused by a semantic gap can be reduced, image emotion prediction is more accurate, and the method is better suitable for large-scale image emotion classification tasks.

The invention discloses an image retrieval method based on a characteristic enrichment area. The method comprises the following steps of firstly, acquiring a candidate characteristic point set by calculating a Hessian matrix and non-maximum value restraint, and acquiring a sub-pixel-level characteristic point set by utilizing a three-dimensional linear interpolation method; secondly, calculating a distribution matrix and an adaption matrix of characteristic points according to coordinate positions of the obtained characteristic points of an image, and by utilizing a maximum sub-matrix and an algorithm, solving a sub-matrix of the adaption matrix, namely the most dense distribution area of the characteristic points as the characteristic enrichment area of the image; thirdly, selecting a shape bottom layer characteristic, a texture bottom layer characteristic and a color bottom layer characteristic for the characteristic enrichment area; finally, measuring similarities according to a Gaussian non-linear distance function, and quickly retrieving the image according to the ascending order of the similarities. According to the method, the calculation complexity of image retrieval can be effectively reduced, and the operation efficiency and the accuracy of the image retrieval are improved.

The invention provides a remote sensing image multi-label retrieval method and system based on a full convolutional neural network. multi-label image retrieval is realized by considering multi-category information of remote sensing images, and the method comprises the following steps: inputting a retrieval image library, and dividing the retrieval image library into a training set and a verification set; Constructing a full convolutional neural network model FCN, and performing network training by using the training set; Performing multi-class label prediction on each image in the verificationset by using FCN to obtain a segmentation result; Carrying out up-sampling on each convolutional layer feature map; Extracting local features of each image in the verification set to obtain feature vectors for retrieval; And finally, carrying out coarse-to-fine two-step retrieval based on the extracted multi-scale features and the multi-label information. According to the method, the full convolutional neural network is used for learning multi-scale local features of the image, multi-label information hidden in the image is fully mined, and compared with an existing remote sensing image retrieval method based on a single label, the accuracy of image retrieval is effectively improved.

The invention discloses an image classification method of chronic venous disease based on multi-scale semantic features, includes multi-scale division of chronic vein disease image to be measured, classification of image block by concept classifier corresponding to each scale on each scale, and obtaining concept class of each image block in the chronic vein disease image to be measured; the frequency of each concept category on each scale is taken as the global representation feature, and the global representation feature on each scale is serially connected to obtain the multi-scale semantic representation of the chronic venous disease images to be measured. The feature selection method based on high-order correlation obtains the best feature subset from the multi-scale semantic representation, and inputs the feature subset into the scene classifier to obtain the classification results of the chronic venous disease images to be tested. The classification result of the invention has high accuracy and strong reliability.

The present invention discloses a cloud image retrieval method based on a shape feature. In effective combination with brightness temperature information of a cloud top of a cloud image, a cloud system is segmented by using an iteration threshold segmentation method; and a shape feature of the cloud system is extracted by using a geometric invariant moment that is relatively low in computational complexity and strong in robustness, thereby overcoming difficulty in analyzing the cloud image by using a conventional cloud image retrieval method based on the shape feature, and especially overcoming problems of method universality, computational complexity and robustness. In addition, according to the method, a bottom cloud image feature is transformed from euclidean space to sparse space, and a cloud image database is retrieved by using a distribution rule of the sparse space, thereby effectively alleviating a semantic gap problem of a conventional retrieval method.

The invention belongs to the technical field of artificial intelligence smart community application, and relates to an unsupervised cross-modal retrieval method based on attention mechanism enhancement, which comprises the following steps of: enhancing visual semantic features of an image, then aggregating feature information of different modals, mapping fused multi-modal features to the same semantic feature space, then, on the basis of a generative adversarial network, adversarial learning is carried out on the image modal and text modal features and the same semantic feature after multi-modal fusion, aligning semantic features of different modals, and finally, generating hash codes by the different modal features after alignment of the generative adversarial network; and performing intra-modal feature and Hash code similarity measurement learning and inter-modal feature and Hash code similarity measurement, so a heterogeneous semantic gap problem between different modalities is reduced, a dependency relationship between different modal features is enhanced, a semantic gap between different modal data is reduced, and semantic common characteristics among different modes can be represented more robustly.

Provided is an image retrieval method based on visual significant integration. A retrieval object is color images. The image retrieval method comprises a training process and a testing process. The training process comprises step 1, building a visual word dictionary: orderly analyzing all images in a training image set so as to set up the visual word dictionary for subsequent retrieval; step 2, utilizing a fusion algorithm of image visual saliency characteristics to obtain a visual saliency picture and obtaining a foreground target picture and a background area picture by partitioning of the saliency picture; step 3, respectively extracting image color characteristics and image texture characteristics of the foreground target picture and the background area picture; step 4, counting a visual word distribution histogram of each picture in a database based on operation of step 1. The testing process also comprises the step 5 of retrieving testing images. The invention provides the image retrieval method based on visual significant integration which is good in partitioning effect and high in retrieval performance.

The invention discloses a universal cross-modal retrieval model based on deep hash. The universal cross-modal retrieval model comprises an image model, a text model, a binary code conversion model and a Hamming space. The image model is used for the feature and semantic extraction of the image data; the text model is used for the feature and semantic extraction of the text data; the binary code conversion model is used for converting the original features into the binary codes; the Hamming space is a common subspace of images and the text data, and the similarity of the cross-modal data can be directly calculated in the Hamming space. According to the universal model for solving cross-modal retrieval by combining deep learning and Hash learning, the data points in an original feature space are mapped into the binary codes in the public Hamming space, similarity ranking is carried out by calculating the Hamming distance between the codes of the data to be queried and the codes of the original data, and therefore a retrieval result is obtained, and the retrieval efficiency is greatly improved. The binary codes are used for replacing the original data storage, so that the requirement of the retrieval tasks for the storage capacity is greatly reduced.

The invention discloses a multi-modal Transformer image description method based on dynamic word embedding, and belongs to the field of artificial intelligence. According to the invention, a model for simultaneously performing intra-modal attention and inter-modal attention is constructed, fusion of multi-modal information is realized, the convolutional neural network is bridged with the Transformer, image information and text information are fused in the same vector space, and the accuracy of language description of the model is improved; a semantic gap problem existing in the field of image description is reduced. Compared with a baseline model using Bottom-up and LSTM, the invention has the advantages that the BLEU-1, the BLEU-2, the BLEU-3, the BLEU-4, the ROUGE-L and the CIDEr-D are all improved.

The invention provides a video retrieving method based on context space. The video retrieving method includes constructing a concept similarity matrix by analyzing basic concept space objects, then subjecting the concept similarity matrix to spectral factorization to obtain a group of basic concept sets, finally mapping known concepts to space spanned by the group of basic concept sets, and measuring similarity among the concept objects on the space. According to the video retrieving method based on the context space, by means of the way of constructing the concept space, problems of locality, inconsistency and the like caused by measurement standards under traditional ontology concept sets are avoided, so that the measurement standards among the concept objects mapped to the space are uniform and wholly consistent, an effective solution is provided for solving video retrieving problems, relationships of basic abstract conceptions are closer, and accuracy of video retrieval is effectively improved.