186 results about "Retrieval algorithm" patented technology

A tag-mediated review system for electronic content that ranks content based upon several ascertainable factors, including the implicit engagement with the content by users (typically online viewers); the expertise and success in the topical domain by reviewers; and the review activity of users or viewers. The system extends implicit metrics to tracking of user interaction including the receipt of video and audio content over the web. Document ranking is mediated by tags that identify the subject matter addressed by an associated document as the ranking mediators for computing content quality scores values for user and reviewer activity. The tag-mediated content quality scores, which are content quality scores for the subject matter area identified by the associated tags, can be exposed to end-users and used directly or indirectly to complement search engine retrieval algorithms.

A confidentiality preserving system and method for performing a rank-ordered search and retrieval of contents of a data collection. The system includes at least one computer system including a search and retrieval algorithm using term frequency and/or similar features for rank-ordering selective contents of the data collection, and enabling secure retrieval of the selective contents based on the rank-order. The search and retrieval algorithm includes a baseline algorithm, a partially server oriented algorithm, and/or a fully server oriented algorithm. The partially and/or fully server oriented algorithms use homomorphic and/or order preserving encryption for enabling search capability from a user other than an owner of the contents of the data collection. The confidentiality preserving method includes using term frequency for rank-ordering selective contents of the data collection, and retrieving the selective contents based on the rank-order.

The invention discloses an image retrieval method, which is realized on the basis of a vocabulary tree level semantic model. Firstly, the characteristics of SIFT (scale-invariant feature transform) comprising color information of an image are extracted to construct the characteristic vocabulary tree of an image library, and a visual sense vocabulary describing image visual sense information is generated. Secondly, the Bayesian decision theory is utilized to realize the mapping of the visual sense vocabulary into semantic subject information on the basis of the generated visual sense vocabulary, a level semantic model is further constructed, and the semantic image retrieval algorithm based on content is completed on the basis of the model. Thirdly, according to relevant feedback of a user during a retrieval process, a positive image expandable image retrieval library can be added, and the high-level semantic mapping can be revised at the same time. Experimental results show that the retrieval method is stable in performance, and the retrieval effect is obviously promoted along with the increasing of feedback times.

The invention discloses an image retrieval algorithm based on a free-hand sketch. According to the method, a metric learning algorithm is used for obtaining a mapping function, the free-hand sketch and a real image are mapped to the same metric space, an L1 normal form in the metric space can directly represent the 'semantic similarity' of the sketch in an original space and a real photo, and the problem of cross-domain matching of the free-hand sketch and the real photo is well solved. Specifically speaking, a positive and negative sample pair is used for training the convolutional neural network, feature vectors of the photo and the free-hand sketch input in the space and labeled to be similar are put together, the feature vectors of images labeled to be not similar are pushed away, and an integrated retrieval method is put out finally. Experiments show that the retrieval performance of the method is greatly improved in the large-scale free-hand sketch retrieval data set.

A system and method to organize information on the internet for rapid and organized retrieval. Registrants of websites can register URLs by specifying the URL and associated descriptors. A bot automatically determines URLs and metadata associated with the registered URL. The URLs and descriptors and/or metadata form a URL database. Search terms entered by users can be indexed against a knowledge database using one or more retrieval algorithms to provide keyword associations. The knowledge database further includes a knowledge acquisition and retrieval system and method that include at least one first memory segment, and a distinct second memory segment, wherein elements of the at least one first memory segment reciprocally associate to elements of the second memory segment. Registrants can modify the knowledge database to incorporate non-traditional associations. The search term, keyword associations, and URL associations provide an organized search result that includes subcategories and cross-categories of information that can be further searched by the user. URL links can be provided in the search results.

The invention discloses an efficient image retrieval method based on an improved SIFT (scale invariant feature transform) feature, relates to the field of image processing and computer vision and belongs to a content-based image retrieval method. The method comprises the steps of establishing a gauss difference scale space, taking scale space detection extreme points as feature points, calculating the direction of the feature points and gradient module values in the direction, generating a global feature vector of the image feature points through a geographical statistical method, conducting global feature vector similarity matching to finish rough retrieval, generating feature descriptors on the basis of rough retrieval, and conducting feature descriptor matching to finish accurate retrieval. The novel image retrieval method is superior to a traditional SIFT algorithm, and is more suitable for requirements of image infringement examination in digital prints than an existing retrieval algorithm.

The invention relates to a road edge detection system and method based on laser radar and fan-shaped space segmentation. The method comprises the steps of 1, a laser radar scanning the surrounding environment of a vehicle to obtain reflection point cloud information and convert the reflection point cloud information into a locally constructed three-dimensional coordinate system; 2, preprocessing the point cloud data, and separating and extracting ground data in each frame of point cloud; 3, dividing the space in the coordinate system into fan-shaped structural bodies according to the data characteristics of the laser radar and the point cloud, and identifying the road extension direction according to the ground information and the fan-shaped structural bodies; 4, extracting road edge candidate points in the point cloud by using a parallel road edge retrieval algorithm; 5, clustering the road edge candidate points, and eliminating an interference point set according to fan-shaped spatial features; and 6, performing B spline curve fitting on the finally determined road edge point to obtain a road edge detection result. The method is high in adaptability, capable of adapting to roadsof various shapes and reducing the influence of obstacles, high in precision and reduction degree, high in reliability and low in error rate.

The invention belongs to the technical field of mode recognition and information processing and provides a mass image retrieval system based on cluster compactness. Steps include 1, calculating local features of images in a sample image library and a test image library; 2, calculating cluster compactness of each image, namely clustering the local features to acquire each type of cluster centers, counting a local feature distribution histogram and spatial statistical information of each cluster, and generating cluster compactness; 3, randomly sampling cluster compactness of the sample image library, clustering components of the cluster centers in the sampled cluster compactness to generate a vocabulary tree, and quantizing the cluster compactness of the images in the test image library to the vocabulary tree to generate corresponding inverted files; 4, retrieving by a modified retrieval algorithm based on the vocabulary tree, namely retrieving, by retrieving the inverted files in the vocabulary tree and calculating the weight of similarity between retrieval images and the image library image cluster compactness.

The invention discloses a cross-modal retrieval algorithm based on mixed hypergraph learning in subspace, based on the cross-modal public subspace learning of canonical correlation analysis. The similarities inside and among modals are calculated by a mapping of the public subspace. A mixed relation matrix is calculated by the similarities inside and among the modals. A mixed hypergraph model is built by the extraction of the relation matrix. And the cross-modal retrieval and sample sequencing are conducted by adoption of hypergraph learning finally. With aiming at cross-modal heterogeneous variations and high order relation among samples, the algorithm instance applies the hypergraph model combined with the cross-modal public subspace learning to the cross-modal retrieval, so that the model is capable of considering the similarity among the modals and the similarity inside the modals simultaneously and giving consideration to the high order relation among a plurality of the samples meanwhile, improving the final precision ratio and the final recall ratio of the cross-modal retrieval. The algorithm is capable of effectively improving the performance of the cross-modal retrieval and greatly enhancing the precision ratio and the recall ratio of the cross-modal retrieval.

The invention discloses a multi-dimensional inverted index and quick retrieval algorithm for large-scale image visual features. The algorithm includes: training a multi-layer codebook required for enhanced residual quantification by visual features of images and building a multi-dimensional inverted index with the trained codebook; according to the trained codebook, quantifying and encoding the image visual features, and according to calculated codes, inserting the codes into corresponding inverted lists in the inverted index; querying the image visual features to query the built multi-dimensional inverted index so as to obtain a query candidate set; optimizing the query candidate set by adaptive hypersphere filtering, and ranking filtered query results so as to finish retrieval of the image visual features. The algorithm has the advantages that as the image features are quantified and encoded, efficiency of quantifying the image features is improved; the multi-dimensional inverted index is built with the generated image codes, the inverted index can be built just by training few codebooks, and speed of building the index structure is increased.

An imaging spectrometer, covering the visible through infrared wavelengths, which disperses the light by a plane diffraction grating behind a wedged optical element. This design uses an achromatic doublet lens with a reflective coating on its convex back surface to produce the spectra on a flat detector. Spatial keystone distortion and spectral smile are controlled to less than one tenth of a pixel over the full wavelength range, facilitating the use of simple retrieval algorithms.

The invention provides an archive management system based on blue light storage and an ant colony search algorithm thereof, comprising an archive collection system, an archive storage server, an optical disk library management server and an optical disk library. Specifically, electronic archive files are assembled, compressed and indexed by the archive collection system in the form of data streams. The ant colony search algorithm has the following specific steps: 1. Constructing a file retrieval path; 2. Updating that pheromone of archives retrieval; 3. Optimizing evaporation factor of searchpath. The algorithm can automatically adjust the information volatility factor, analyze the keywords, and call the relevant persistence layer data through the algorithm. The archives management systemadopts the optimized retrieval arithmetic, According to the characteristics of archives, ACA algorithm is used, its process and algorithm are analyzed, by combining the pheromone volatility, the effect of the pheromone volatility factor on the efficiency of algorithm retrieval is improved, and by combining differential evolution processing, efficient retrieval efficiency of massive data is realized.

An image index algorithm based on information abrupt includes converting color data of sample image inputted by user from GRB space to be HSV space and making normalized treatment on it then carrying out initial division on image according to pixel correlation degree and information abrupt, carrying out circulating combination as per similarities of divided pixel blocks, dividing image to be 3x3 subblock and extracting out feature vectors of nine regions separately, selecting interested region from nine regions by user and comparing it with image to be indexed on their proceed similarities to obtain index result of proceed image.

The invention discloses a method for providing network service resources. The method comprises the following steps: firstly, classifying the network service resources; then, providing a retrieval scheme according to the interest of a user, and providing the classified network service resources according to the retrieval scheme; firstly extracting feature vectors of to-be-classified service resources when the network service resources are classified, then, computing the probability of each attribute in each feature vector in each category and the weight of each attribute, then, acquiring the probability that each attribute belongs to each category by utilizing a weighting Naive Bayes formula, and selecting the maximum as the classification category of each service resource. According to the method disclosed by the invention, when the service resources are classified, by utilizing the bayesian classification algorithm and combining the calculation of attribute similarity, the classification accuracy of the service resources is improved, and when the service networks are provided for the user, a user interest-based personalized service resource retrieval is explored under the framework of a vector space model, so that the complexity of time and space in the retrieval algorithm is reduced, and the retrieval efficiency is improved.

The invention provides a high-dimension vector rapid searching algorithm based on a block distance and belongs to the field of data processing such as multimedia information searching, intelligent information processing, data mining, and the like. In the invention, an index structure Block B-tree which is converted from high dimension to one dimension and is based on the block distance is provided; a high-dimension vector is mapped into one-dimensional key values by adopting the block distance of the high-dimension vector to a reference point; and the index structure B+-tree is used for managing the key values, and each key value of a leaf node layer is bound with a pointer pointing to a corresponding high-dimension vector. During searching, the same mapping method is used for mapping a query vector into one-dimension query key values, and then similarity calculation is only performed on the high-dimension characteristics of the key values close to the query key values, thereby reducing the calculated quantity and greatly increasing the searching speed. In a similarity matching algorithm of the high-dimension vector, the block distance is a frequently-used measurement way, the operation of the algorithm is simple, and the searching efficiency is higher, while most of the current index structures are provided based on Euclidean distance matching measurement. The index structureprovided by the invention not only supports searching based on the Euclidean distance matching way but also directly supports searching based on the block distance measurement way.

A system is provided for integrative analysis of intrinsic and extrinsic audiovisual information, such as a system for analysis and correlation of features in a film with features not present in the film but available through the Internet. The system comprises an intrinsic content analyser communicatively connected to an audio-visual source, e.g. a film source, for searching the film for intrinsic data and extracting the intrinsic data using an extraction algorithm. Further, the system comprises an extrinsic content analyser communicatively connected to an extrinsic information source, such as a film screenplay available through the Internet, for searching the extrinsic information source and retrieving extrinsic data using a retrieval algorithm. The intrinsic data and the extrinsic data are correlated in a multisource data structure. The multisource data structure being transformed into high-level information structure which is presented to a user of the system. The user may browse the high-level information structure for such information as the actor identification in a film.

The invention discloses a multi-outlet routing selection method, which comprises the following steps of performing a path detection module according to each network outlet server to realize real-timedetection of a destination address, uploading the destination address to a path management center, judging a routing strategy by the path management center, and determining an optimal path of the samedestination address in a plurality of network outlets. A user-defined module of a netfilter adopts an interval tree retrieval algorithm; therefore, the destination addresses of all the data packets are quickly classified and marked (a network outlet is specified), the user permission level is dynamically matched according to the source addresses of the data packets, and the corresponding available network outlet is specified based on the user permission level and the identification code of the network outlet server. Routing policy routing realizes routing outlet selection according to the fwmarkb packet identifier, and the method can adapt to dynamic adjustment of multiple outlets of the network, and realizes high-precision dynamic automatic selection of the multi-outlet routing.

The invention discloses an urban rainfall inversion algorithm based on microwave attenuation characteristic response fingerprint identification, includes the acquisition and preliminary processing ofmicrowave signals, the separation and classification of clutter signals, the establishment of clutter characteristic response fingerprint database, the identification and removal of clutter, rainfallinversion, scale matching of multi-source rainfall data, the optimization of model parameters and the real-time correction of inversion results. The invention can quickly judge and remove the influence of different types of clutter in the microwave attenuation signal under the urban environment, and provides urban multi-resolution rainfall big data with a spatial resolution of 100 meters-200 meters and a time resolution of 5 minutes-10 minutes.

The invention discloses a quantitative description method for an assembly model during model retrieval, which is used for solving the technical problem of low retrieval efficiency of a traditional quantitative description method for an assembly model. In the technical scheme, the quantitative description method comprises the following steps calculating a Euclidean distance between two arbitrary points on the surface of a part model; extracting a distance distribution histogram; expressing each part as a point in a 1024-dimensional space; carrying out separate and accurate quantitative description on the part model by using such point; expressing the assembly model as a set with a series of points, wherein each point expresses a part in the assembly model; and carrying out quantitative description on the assembly model in a point set mode. By the quantitative description method, the description accuracy is ensured, and meanwhile, a description result of the part model is only dependent on the model per se and is not affected by other factors; and compared with the background art, the quantitative description method has the advantages that the point set mode is used for quantitatively describing the assembly model, the complexity of a retrieval algorithm is reduced, and the retrieval efficiency is improved.