477results about How to "The classification result is accurate" patented technology

A visual commerce engine can provide information related to an object based on an image of the object. The visual commerce engine receives from a user device an image of an object and a location within the image associated with the object, and analyzes the image to detect potential objects depicted in the image. From this set a detected object can be selected based on the received location's proximity to any of the detected potential objects. A description of the detected object can then be determined and compared with a library of objects to identify similar, identical, or related objects.

Embodiments of the present invention provide a signal classification method and device, and encoding and decoding methods and devices. The encoding method includes: dividing a current frame into a low-frequency band signal and a high-frequency band signal; attenuating the high-frequency band signal or a to-be-encoded characteristic parameter of the high-frequency band signal according to an energy attenuation value of the low-frequency band signal, where the energy attenuation value indicates energy attenuation of the low-frequency band signal caused by encoding of the low-frequency band signal; and encoding the attenuated high-frequency band signal or the attenuated to-be-encoded characteristic parameter of the high-frequency band signal. The technical solutions according to the embodiments of the present invention can improve the effect of combining the low-frequency band signal and the high-frequency band signal at the decoder.

The invention discloses a local spline embedding-based orthogonal semi-monitoring subspace image classification method. The method comprises the following steps of: 1) selecting n samples serving as training sets and the balance serving as testing sets from image data sets, wherein the training sets comprise marked data and unmarked data; 2) building an extra-class divergence matrix and an intra-class divergence matrix by using the marked data; (3) training data characteristic space distribution by using a whole and building a Laplacian matrix in a local spline embedding mode; 4) according to a local spline, embedding an orthogonal semi-monitoring subspace model, and searching a projection matrix to perform dimensionality reduction on the original high dimension characteristic; 5) building a classifier for the training samples after the dimensionality reduction by using a support vector machine; and 6) performing the dimensionality reduction on the testing sets by using the projection matrix and classifying the testing sets after the dimensionality reduction by using the classifier. In the method, the information, such as image sample marking, characteristic space distribution and the like, is fully utilized; potential semantic relevance among image data can be found out; and image semantics can be analyzed and expressed better.

The invention discloses a method of image classification based on a visual attention model in the technical field of image classification. The method of the invention comprises the following steps: step 1: certain quantity of images are randomly selected from a selected image database to be used as training samples; step 2: a characteristic vector of each image based on the visual attention model and the overall scarcity is captured; step 3: a characteristic vector of each image based on the visual attention model and the overall scarcity is calculated to the image to be classified; and step 4: the characteristic vectors captured in step 2 and step 3 are transmitted to a classifier to be classified; finally, the result of the classification of the image to be classified is obtained. The method can classify the image by capturing visual characteristic characters at a high level from the characters at a bottom level so as to ensure more precise result of the classification.

The invention discloses a method for classifying polarimetric SAR (synthetic aperture radar) images on the basis of Cloude decomposition and K-wishart distribution and mainly solves the problem that the prior art is poor in classification effect and high in computation complexity. The method includes the steps of firstly, reading a polarimetric SAR image to be classified, subjecting each pixel of the image to Cloude decomposition to obtain an entropy H and a scattering angle alpha; secondly, initially partitioning the polarimetric SAR image according to the values of the entropy H and the scattering angle into eight classes; thirdly, subjecting results of eight classes of the whole polarimetric SAR image to iteration to obtain more accurate classification results. Compared with classic classification methods, the method is stricter in partitioning the polarimetric SAR image, better in classification effect, lower in computation complexity and applicable to terrain classification and target recognition of the polarimetric SAR images.

The invention discloses a network user classification method and device. The network user classification method comprises the following steps of: generating behavior characteristic vectors of users based on the network behaviors of the users; clustering based on the behavior characteristic vectors of the users so as to construct a user-topic matrix; determining the webpage category to which each user belongs on a basis of the user-topic matrix, wherein the network behaviors of the users comprise network access behaviors and search behaviors, and each element value of the user-topic matrix respectively represents the correlation degree between each user and each topic. According to the method and device disclosed by the invention, on a basis of various types of data of the user search behaviors and access behaviors, overall user behaviors are covered within a smaller calculation cost by the adoption of a user-topic model, so that the user information is sufficient to use, and a more accurate user classification result is faster to obtain.

A text classification apparatus directed to a plurality of languages, includes a unit for extracting information for converting a word from non-classified (unlabeled) texts, in a plurality of languages, into a word sense, a unit for learning a classification knowledge at a word sense level after converting a word extracted from a labeled text into a word sense, a unit for learning a classification knowledge at a word level from the labeled text, a unit for learning the classification knowledge at the word level from the classification knowledge at the word sense level and information on a relation between words extracted from the unlabeled text, and a unit for combining the respective classification knowledges to assign a category.

The invention discloses a polarization synthetic aperture radar (SAR) image classification method based on spectral clustering. The polarization SAR image classification method mainly solves the problem that an existing non-supervision polarization SAR classification method is low in accuracy. The polarization SAR image classification method comprises the steps of extracting scattering entropy H of representation polarization SAR target characteristics to serve as an input characteristic space of a Mean Shift algorithm combining with space coordination information; diving in the characteristic space with the Mean Shift algorithm to obtain M areas; choosing representation points of all areas on the M areas to serve as spectral clustering input to spectrally divide all areas, and further finishing spectral clustering on all pixel points to obtain pre-classification results; and finally classifying the whole image obtained from the pre classification with a Wishart classifier capable of reflecting polarization SAR distribution characteristics in an iteration mode to obtain classification results. Tests show that the polarization SAR image classification method is good in image classification effect and can be applied to non-supervision classification on various polarization SAR images.

The invention discloses a correction-based K nearest neighbor text classification method, which comprises the following steps of: pre-processing texts, namely performing word segmentation on each document in a training text set, removing stop words, and performing projectized expression on the texts; selecting text characteristics, namely reducing dimensions of text vectors, and selecting document characteristics closely related with a document theme concept as few as possible; and finally, performing classification by using a correction-based K nearest neighbor text classification algorithm construction classifier to obtain a classification result. The method has accurate classification result.

The invention provides a handwriting recognition method based on an encrypted neural network. The handwriting recognition method comprises the following steps: 1, preprocessing feature data and labeldata; 2, constructing a deep learning model, and training hyper-parameters; 3, performing homomorphic encryption on the preprocessed data; 4, improving matrix point multiplication and an activation function, and constructing an encrypted neural network; and 5, carrying out classification identification by utilizing an encryption neural network, and combining a homomorphic encryption algorithm witha neural network model to realize protection of data privacy and prevent leakage of data information, so that the method is mainly applied to the field of information security and artificial intelligence crossing.

The invention relates to a rock fragment size classification method based on multiple features and segmentation recorrection. The method comprises the following steps that (1) an original rock fragment size image is cut and blocked to generate subblock images of 64*43; (2) color feature extraction is carried out on the subblock images; (3) texture feature extraction is carried out on the subblock images; (4) feature selection is carried out on color features and texture features; (5) a classifier is designed through a support vector machine (SVM), and rough classification on the subblock images is completed; (6) watershed segmentation is carried out on the original rock fragment size image, and each obtained rock area is marked as blob; (7) on the basis of segmentation, a result obtained through rough classification is corrected and adjusted to improve classification performance and obtain a final classification result. According to the classification method, different classification algorithms are synthesized, efficiency and accuracy are high, cost is low, and the real-time performance requirement of a mining field is met.

Embodiments of the invention disclose a face detection method and apparatus, and terminal equipment, and are applied to the technical field of information processing. The face detection apparatus performs feature sampling according to feature information of an image in a face candidate frame of a to-be-processed picture and obtains a plurality of pieces of sampling feature information, then a detection score is obtained according to a preset calculation function and the plurality of pieces of sampling feature information, and finally, the image in the face candidate frame is classified according to the detection score, namely, a classification result whether the image in the face candidate frame is a face can be obtained. Therefore, the plurality of pieces of sampling feature information can be obtained through feature sampling, features of the image in the face candidate frame are selectively expressed, and the finally obtained classification result is accurate; and the detection score of the image in the face candidate frame is obtained through the plurality of pieces of sampling feature information, and the plurality of pieces of sampling feature information obtained through anyfeature sampling mode can have the same detection scores so that the precision of the finally obtained classification result is improved.

The invention discloses a polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix. The polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix mainly solves the problem that the prior art is low in the classification correct rate for the same natural object having obvious difference on the scattering information and different natural objects having similar scattering information. The polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix includes the following implementation steps: 1, filtering an original polarized SAR image; 2, extracting the polarized shallow-layer characteristics of the polarized SAR image after filtering; 3, fusing the shallow-layer characteristics with the polarized SAR data after filtering to construct training simples and a test samples; 4, learning the training samples through a convolution neural network; and 5, classifying the test samples by means of the convolution neural network which is obtained through learning, and obtaining the final polarized SAR natural object classification result. The polarized SAR classification method based on deep learning of shallow-layer characteristics and T-matrix is high in the classification correct rate of the polarized SAR target natural objects, has good experimental effect on classification of the natural object targets of a large area, and can be applied to target identification and natural object classification of a large scene.

The invention discloses a method for classifying texture images on the basis of a local edge pattern. The method includes steps of inputting original texture images of the images to be classified; dividing the original texture images into n image blocks; respectively computing local edge pattern texture spectrum features of the original texture images and the n image blocks on the basis of m types of texture primitives with different sizes, and serially connecting the local edge pattern texture spectrum features with one another to obtain overall fusion local edge pattern texture spectrum features of the images to be classified; classifying the images to be classified into categories of training images with the minimum Canberra distances according to the overall fusion local edge pattern texture spectrum features of the images to be classified. Length and width pixels of the size of each texture primitive are even numbers, and the minimum texture primitive contains 2X2 pixels. The invention further discloses a system for classifying the texture images on the basis of the local edge pattern. The method and the system have the advantages that texture information acquired by the method and the system is rich and robust, and the texture image classification accuracy is high.

The invention provides a feature classification method for a liver ultrasonic image. The method comprises the steps of S1, for a to-be-processed ultrasonic image comprising a liver section/part, automatically extracting a liver capsule line from the ultrasonic image; S2, based on the extracted liver capsule line, selecting multiple sampling points, and generating a triple feature of each samplingpoint; S3, extracting each triple feature, and classifying the extracted triple features; and S4, according to a classification result of all the extracted triple features, determining the type of theultrasonic image. According to the method, classification results of all image blocks are combined to obtain an accurate classification result; the noise interference is reduced; automatic identification and classification are realized; and the labor cost is reduced.

The invention discloses a time sequence remote sensing image-oriented land utilization classification method, and the method comprises the following steps: carrying out principal component analysis onmultispectral images forming time sequence remote sensing data to obtain images of three principal components; pre-training each three-waveband image, and extracting a feature image; sequentially inputting the feature images into a semi-supervised convolution long-term and short-term memory network model for training according to a time sequence; and utilizing the trained model to predict and classify the image of the last time phase to obtain a classification result. According to the invention, time context information of a time sequence and space and spectral characteristic information of aremote sensing image are comprehensively considered; the problems existing in the prior art are well solved by utilizing the pre-training model and the semi-supervised classification learning mode, so the land utilization classification method is more suitable for classification scenes with less training sample data and missing or partially missing remote sensing image data, and a better land utilization classification result is obtained.