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379results about How to "Improve classification efficiency" patented technology

Advertisement classification method and device based on webpage characteristic

The invention discloses an advertisement classification method and system based on a webpage characteristic. The method comprises the following steps of: extracting the webpage characteristic information from the webpage sample information and extracting the advertisement characteristic information from the advertisement sample information; mapping the webpage characteristic information and the advertisement characteristic information to a common characteristic space by use of a transfer learning method to obtain the webpage sample information and advertisement sample information mapped to the common characteristic space; training a classifier based on the webpage sample information mapped to the common characteristic space and a current training set, and classifying the advertisement sample information according to the trained classifier to obtain a classification result; establishing a link network between the webpage and the advertisement according to the historical release and click data of the advertisement sample information so that the classification result is transmitted along the link network and the corrected classification result is obtained; and updating the training set according to the corrected classification result. Through the invention, existing mark data can be sufficiently utilized, and a large amount of repeated work is avoided.
Owner:亿赞普(北京)科技有限公司

Method for identifying and positioning power transmission line insulators in unmanned aerial vehicle aerial images

InactiveCN105528595AImprove recognition rateTo achieve the purpose of texture analysisScene recognitionRobustificationData set
The invention belongs to the technical field of image processing, discloses a method for identifying and positioning power transmission line insulators in unmanned aerial vehicle aerial images, and solves the problems in the prior art that the detection precision of an identification algorithm of the insulators is not high, the robustness is low, and the identification algorithm is easy to be affected by sample number. A group of Gabor wavelet basis with different sizes and different directions and training sample images are taken as convolutions so as to form a group of characteristic vectors which accurately describe sample image texture characteristics. A random forest machine learning algorithm with a semi-supervised learning mode is used to train sample data sets of the known category and the unknown category so as to obtain an insulator identification model. Through the mode from left to right and from top to bottom, a detection window with the same size as the training sample traverses the input images with different sizes. The detection window combining the identification model detects and positions the positions of the insulators in the input images with different sizes. And finally the accurate positions of the insulators in the input image with the original size are determined by using a non-maximum inhibition method.
Owner:CHENGDU TOPPLUSVISION TECH CO LTD

Method and system for coal and rock boundary dividing based on coal and rock image feature extraction and classification and recognition

Disclosed are a method and a system for coal and rock boundary dividing based on coal and rock image feature extraction and classification and recognition. The method includes firstly, photographing coal and rock images on a coal mining working face, extracting texture feature information of the coal and rock images, and constructing a feature vector, wherein the texture feature information comprises the original images and the angular second moment, the contrast, the correlation, the mean value and the variance of every low-frequency coefficient image with Daubechies wavelet decomposition scale, and the texture feature information also comprises the total mean value and the total variance for calculating high-frequency coefficient images in horizontal, vertical and diagonal directions; and lastly, creating a best classifier to recognize the coal and rock boundary. The system comprises an image acquisition module, a feature module, a classification and recognition module, a result display interactive module, a memory module and a central control module. The method and the system for the coal and rock boundary dividing based on the coal and rock image feature extraction and the classification and recognition have simple calculation, less human intervention and low cost, can improve classification accuracy and efficiency of the coal and rock images in complex environments effectively, and provides accurate and reliable coal and rock boundary information.
Owner:CHINA UNIV OF MINING & TECH (BEIJING)

UAV onboard multi-target detection tracking and indication system and method

The invention discloses an UAV onboard multi-target detection tracking and indication system and method, and belongs to the technical field of target detection tracking and indication. The invention discloses an UAV onboard multi-target detection tracking and indication system comprising a multi-target detection and tracking system and a multi-target laser indication system. The multi-target detection and tracking system comprises an infrared camera, a visible light image sensor and a high-speed parallel image processing and tracking feedback control circuit. The multi-target indication systemincludes an integrated laser, a fast reflector, a fast reflector control module, and a laser control module. In order to improve laser indication accuracy, the UAV onboard multi-target detection tracking and indication system also includes a laser pointing control system. The invention also discloses an UAV onboard multi-target detection tracking and indication method based on the UAV onboard multi-target detection tracking and indication system. The UAV onboard multi-target detection tracking and indication system and method realize multi-target all-weather detection tracking and high-precision stable laser indication under the condition of a UAV onboard platform.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY

Image classification method and device, readable storage medium and terminal equipment

The invention relates to the technical field of image processing, in particular to an image classification method and device, a storage medium and terminal equipment. The image classification method provided by the invention comprises the following steps: obtaining a to-be-classified image; inputting the to-be-classified image into a trained image classification model to obtain an image label output by the image classification model, wherein the image classification model comprises a teacher model and a student model, and the teacher model is a convolutional neural network model obtained by carrying out weak supervision training by utilizing noise data and non-noise data, and the student model is a model obtained by carrying out joint training according to a knowledge migration mechanism and a multi-task learning mechanism based on the teacher model; obtaining a classification result corresponding to the to-be-classified image according to the image label, using the teacher model to perform knowledge migration, and using the noise data and the non-noise data to perform multi-task learning training, thereby improving the classification efficiency and the classification accuracy of the image classification model.
Owner:SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI

Method for carrying out mangrove forest map making on intermediate resolution remote sensing image by utilizing object-oriented classification method

InactiveCN103000077AHigh precisionOvercome missing pointsMaps/plans/chartsMangroveProblem of time
The invention relates to a method for carrying out mangrove forest map making on intermediate resolution remote sensing image by utilizing object-oriented classification method, which relates to a method for mangrove forest map making and solves the problems of time and labor waste, bad timeliness and serious neglected and wrong classification of mangrove forest in positioning and map making of mangrove forest map making through conventional means at present. The method comprises the following steps: firstly, carrying out ortho-rectification and geometric exact correction on Landsat TM data so as to obtain Landsat TM images after registration; secondly, carrying out multi-layered multi-dimensioned division on the Landsat TM images after registration, wherein each division unit is used as an object; thirdly, extracting textural and topological characteristics, and calculating normalized vegetation index and ground surface humidity index; fourthly, removing a non-vegetated object so as to obtain a vegetated object; fifthly, extracting a mangrove forest object from the vegetated object; sixthly, exporting the mangrove forest object so as to generate a mangrove forest vector; and seventhly, manufacturing a mangrove forest thematic map. The method disclosed by the invention is used for mangrove forest map making.
Owner:NORTHEAST INST OF GEOGRAPHY & AGRIECOLOGY C A S

Method for recognizing and classifying road barriers based on video

ActiveCN103914698APrevent integrationRealize classification recognitionCharacter and pattern recognitionFeature extractionRecognition algorithm
The invention discloses a method for recognizing and classifying road barriers based on video. According to the method for recognizing and classifying the road barriers based on the video, according to the urban road monitoring video, a barrier feature extraction and recognition algorithm is studied, a hybrid Gaussian modeling method self-adaptive to background update is provided, and the road background is updated selectively according to static barrier targets obtained through detection; a moving target segmentation method based on concave-convex outline characteristics of the targets is provided, further accurate extraction and separation of the moving targets are achieved through the method, and a foundation is laid for outline-based segmentation of a blocked target; an algorithm for automatic detection of an ROI of a road is provided, and automatic extraction of the ROI of the road in a monitoring image is achieved. A road barrier classifying method involving self-adaptive clipping of the ROI is adopted, road barriers are recognized and classified to be illegally parked vehicles and scattered objects. By the adoption of the method for recognizing and classifying the road barriers based on the video, the barrier handling efficiency of the traffic department can be improved easily, and a foundation is laid for preventing road accidents.
Owner:UNIV OF SCI & TECH BEIJING

Remote sensing image classification method based on active deep learning

The invention discloses a remote sensing image classification method based on active deep learning. The remote sensing image classification method includes the steps that (1), remote sensing image data to be classified are selected; (2), the remote sensing image data are processed by utilizing an algorithm configured in advance; (3), the optimal sample B is selected from unmarked samples U by applying the active learning algorithm nEQB; (4), the optimal sample B is subtracted from the unmarked samples U to obtain a new unmarked sample set U', and the optimal sample B is added to marked samples L to obtain a new marked sample set L'; (5), the step (2) is executed again, the process continues to be circulated, the circulation is completed until the unmarked sample set U'is a null set or a preset learning stopping index is met, and classification accuracy and a classification result graph matched with the classification accuracy are output. The remote sensing image classification method has the advantages that through deep learning and active learning, the defects caused by using unsupervised learning and supervised learning can be overcome, and the classification accuracy of the data is effectively improved.
Owner:INST OF REMOTE SENSING & DIGITAL EARTH CHINESE ACADEMY OF SCI

Preparation technology of super pure coal

The invention discloses a preparation technology of super pure coal, and is applicable to the technical field of mineral separation. The preparation technology comprises the steps that after minus 50 mm low-ash anthracite is crushed through a high-efficiency fine crusher, the crushed low-ash anthracite is introduced into a three-product interference bed for separation, and coarse clean coal, medium coal and recrement are obtained; coarse clean coal is classified through a three-product vortex sieve, and coarse clean coal remaining on the sieve is dehydrated through a coal slime centrifuge to obtain a coarse-particle super pure coal product; and after primary coarse grinding, medium coal is introduced into a sieve bend for classification, oversize material on the sieve bend is introduced into a spiral separator for tailing discharging, and after secondary fine grinding, a light product is introduced into a flotation device for flotation together with screen underflows of the three-product vortex sieve, overflows, the centrifugate of the coal slime centrifuge, and screen underflows of the sieve bend, and a fine-particle super pure coal product is obtained. According to the preparation technology, a two-stage ore grinding technology is adopted, so that an overgrinding phenomenon which often occurs during open-circuit grinding is avoided; tailings are discharged during primary ore grinding, so that efficiency and energy consumption are achieved; the two-stage floatation is adopted, so that low-ash clean coal can be obtained; and compared with a traditional separation technology, the preparation technology has the advantages that the principle of more crushing and less grinding is better met, efficiency and energy consumption are achieved, and the economic benefits are remarkable.
Owner:CHINA UNIV OF MINING & TECH

Audio classification method, device and computer readable storage medium

The invention discloses an audio classification method, device and a computer readable storage medium, and belongs to the technical field of electronics. The method comprises: collecting an audio signal; intercepting or supplementing the audio signal to adjust the duration of the audio signal to a preset duration; converting the audio signal to a target audio according to the frequency informationof the audio signal; extracting audio features of the target audio through a convolutional network contained in a preset classifier; extracting time-order features of the audio features through a threshold circulation network contained in the preset classifier; and determining a probability that a category of the target audio is a preset category identified by each of multiple preset category identifiers through a fully-connected network contained in the preset classifier according to the time-order features; and determining the preset category identified by a preset category identifier having the highest probability among the multiple preset category identifiers as the category of the target audio. With the adoption of the method, segmentation of the target audio is avoided, the integrity of the target audio is preserved, and the classification accuracy is relatively high.
Owner:TENCENT MUSIC ENTERTAINMENT TECH SHENZHEN CO LTD

Alzheimer's disease multi-classification diagnosis system based on deep study

The invention relates to an Alzheimer's disease multi-classification diagnosis system based on deep study. The Alzheimer's disease multi-classification diagnosis system comprises an image characteristic extracting module, an index characteristic selecting module, a vector linear merging module and a disease classification and diagnosis module, wherein the image characteristic extracting module is used for extracting characteristic vectors of a cerebral three-orthogonal plane MRI image according to a neural network model; the index characteristic selecting module is used for selecting checking indexes according to medical pertinent literatures to form index characteristic vectors; the vector linear merging module is used for adopting a multivariate data linear merging method based on canonical correlation analysis to merge the characteristic vectors of the image and the index characteristic vectors; and the disease classification and diagnosis module is used for inputting the merged vectors to a multi-classification classifier to distinguish the three stages of the Alzheimer's disease. The Alzheimer's disease multi-classification diagnosis system disclosed by the invention can assist the multi-classification diagnosis of the Alzheimer's disease.
Owner:DONGHUA UNIV +1

Coin sorting device

The present invention provides a coin sorting device, which comprises a bearing platform (2), a coin separating and extracting mechanism, a sensor, a coin transmission mechanism, a coin collecting mechanism and a control system. The coin separating and extracting mechanism is mounted on the bearing platform (2). The coin transmission mechanism and the coin collecting mechanism are sequentially arranged on the downstream of the coin separating and extracting mechanism. The sensor is located on the circumferential part of the transmission mechanism. In combination with the classification information fed back by the sensor, the control system respectively sends a control instruction to the coin separating and extracting mechanism, the coin transmission mechanism and the coin collecting mechanism to complete the coin separating operation, the coin transmitting operation and the coin collecting operation. According to the technical scheme of the invention, the traditional manner that banknotes must be manually put and sorted one by one for multiple times is replaced by the above process. Therefore, the banknote sorting efficiency is improved when a larger number of banknotes are to be sorted. Moreover, the coin sorting device is simple in overall structure, easy to install and debug, and strong in applicability.
Owner:SHANGHAI JIAO TONG UNIV

Intelligent logistics classification system

The invention discloses an intelligent logistics classification system. The intelligent logistics classification system comprises a first conveyor belt, a first motor and a rotary table. The feed end and the discharge end of the first conveyor belt are provided with a first support and a third support correspondingly. The upper end of the first support is provided with an opposite-type photoelectric sensor, and the upper end of the third support is provided with a bar code scanner. A second conveyor belt is arranged on the rotary table. The discharge end of the second conveyor belt and the discharge end of the first conveyor belt are connected in a matched mode. A second support is arranged at one end of the second conveyor belt. The top end of the second support is provided with a reflective photoelectric sensor. A collection barrel is arranged below the discharge end of the second conveyor belt. A magnetic sensor is further arranged on the upper surface of a shell on a base and is used for positioning the rotary table. The sensors and the bar code scanner are all electrically connected with a PLC host. The intelligent logistics classification system can achieve intelligent classification of different types of packages, avoids the error probability to the maximum extent, improves the logistics classification efficiency, reduces the amount of labor of staff and is suitable for being extensively popularized.
Owner:梁春孟 +4

Corrosion level information processing method and system based on image recognition

The invention belongs to the technical field of metal corrosion morphology grade judgment, and discloses a corrosion grade information processing method and system based on image recognition, and themethod comprises the steps: obtaining a labeled laboratory metal morphology picture as a data set; carrying out the de-duplication on the obtained training data set, randomly selecting a half of images of each category as a test set, and taking other images as training set images; expanding the training data set, namely carrying out the data enhancement; building a convolutional neural network model by using a Keras framework, carrying out model training, and storing optimal model parameters; and analyzing the grade of the corrosion morphology of the metal surface by utilizing the trained optimal model. According to the method, the judgment of the metal corrosion morphology grade is realized by using a computer vision technology, so the judgment efficiency is greatly improved and the judgment uniformity and objectivity are ensured under the condition of ensuring the judgment accuracy. The method can assist engineers engaged in material research in judging the metal corrosion grade, andvarious difficulties of a previous judgment method are solved.
Owner:HUAZHONG UNIV OF SCI & TECH

Aluminum plate surface defect classification method based on BP neural network and support vector machine

ActiveCN104766097AAchieve high recognition classificationExcellent Classification ModelCharacter and pattern recognitionClassification methodsNetwork classification
The invention discloses an aluminum plate surface defect classification method based on a BP neural network and a support vector machine. The method comprises the steps that feature values of aluminum plate surface defects are extracted as the input quantity of a BP neural network classification model, and oil spots and the first class of defects are adopted as the output quantity to construct the BP neural network classification model; a plurality of support vector machine classification models are constructed through the first class of defects in a one-to-one classification method; learning samples are obtained, and the BP neural network classification model and the support vector machine classification models are trained; the oil spots and the first class of defects are classified through the BP neural network classification model, the BP neural network classification model is regarded as a testing sample of the oil spots to be removed, and the rest of the first class of defects are classified again through the support vector machine classification models; a classification result is obtained finally through statistics. According to the method, the recognition and classification rate of the oil spots on the surface of a cold rolling aluminum plate is improved, meanwhile, the overall recognition rate of the cold rolling aluminum plate surface defects is improved, and the method can be used for recognizing and classifying other metal surface defects and is simple and easy to implement.
Owner:山东颐泽天泰医疗科技有限公司
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