34 results about "Linear discrimination analysis" patented technology

Provided is an image feature extraction method based on global and local structure fusion, characterized by comprising: 1) constructing a weight adjacent map; 2) determining laplacian matrix of similar matrix, degree matrix and images, 3) determining scatter matrix inside the kind and between the kind; 4) determining projection matrix, 5) identifying. The invention provides a feature extraction method of fusing the global structure information and the local structure information, wherein complex features fused of the global feature and the local feature are extracted, thereby the method has strong resolving power. The method not only has the characteristics of holding the reflection method locally, namely holding the characteristics of manifold structure of data; moreover has the characteristics of linear discrimination analysis method, namely assembling the date of the kind more compact to enlarge the distance between the kinds. The invention is applied in image recognition, thereby increasing identifying performance.

The invention provides a printed font character identification method based on Arabic character set which comprises, extracting region information, character font information, and constituent part information unique for Arabic character set, carrying presorting, determining character class subset of the input character, extracting direction characteristic for reflecting the character stroke composition information, finally employing two steps of characteristic optimization processing including, characteristic dressing, eigen transformation by integrating linear discrimination analysis (LDA) and K-L transformation, finally proceeding categorization judgment through modified quadratic discriminating function (MQDF) statistical classification device.

The invention belongs to the field of mode recognition and a brain-machine interface and discloses a motor imagery electroencephalogram voting strategy sorting method based on extreme learning machines. The motor imagery electroencephalogram voting strategy sorting method comprises the following steps: dividing an original motor imagery electroencephalogram into S sections of sub-signals; carrying out dimensionality reduction on each section of sub-signal by a principal component analysis method; carrying out secondary dimensionality reduction on a feature vector subjected to the dimensionality reduction by a linear discrimination analysis method; carrying out the same processing on the S sections of sub-signals to finally obtain S (K-1)-dimensional feature vectors, and combining the S (K-1)-dimensional feature vectors to finally obtain an S*(K-1)-dimensional feature; and transmitting the S*(K-1)-dimensional feature into a plurality of ELM (Extreme Learning Machine) sorting devices so as to obtain a final sorting result by utilizing a voting sorting strategy. The invention provides a voting sorting strategy based on the ELMs; compared with a traditional multi-time ELM average accuracy scheme, the method provided by the invention has the advantages that the sorting accuracy is improved under the condition of not influencing the training sorting low consumption.

The invention provides a feature extraction method of a radiated noise signal of an underwater target, comprising the steps of: firstly researching a visualization method of an acoustic signal to obtain a time-frequency visual image; then carrying out visual feature extraction method research including feature extraction and image centre torque feature extraction based on image texture by regarding a spectrogram in the time-frequency visual image as a searched object; on the basis, respectively utilizing principal component analysis (PCA) and linear discrimination analysis (LDA) algorithms to carry out optimal selection and dimension reduction on the visual feature obtained by the method, and finally applying the visual feature to simulation of underwater target identification. The experimental results show that the average recognition rates of three types of underwater noise signals under the mixed feature can be up to 91.97%, and the recognition rates of the selected visual feature parameters are obviously improved compared with the feature parameters extracted by the conventional method.

The invention discloses a manual alphabet identification method based on an RGB-D image, and belongs to the technical field of behavior identification of computer vision. According to different types of hand texture information of different types of manual alphabets, histogram-of -oriented-gradient characteristics and super-normal-vector characteristics of an RGB-D image video frame obtained through an RGB-D camera are extracted; a principal component analysis and a linear discrimination analysis are combined for characteristic attribute optimizing processing, so that a characteristic attribute with saliency is obtained, and BoW sparse representation is conducted on the characteristics; a data contrast mining technology is used for obtaining representative exemplars in all the types of manual alphabet; finally, a hierarchy judgment policy based on a greedy thought is adopted, the manual alphabets easy to classify are quickly classified through a nonparametric k-Nearest Neighbor Algorithm classifier firstly, and the manual alphabets difficult to classify are judged through a trained support vector machine model based on templates. Compared with the prior art, the manual alphabet identification method is high in both identification accuracy and identification efficiency.

The invention discloses a face recognition method comprising the following steps of: establishing a face training database including a plurality of face training images, structuring a projection matrix based on all the face training images of the face training database, and projecting all the face training images in the face training database onto the projection matrix to obtain the characteristic matrixes of all the training images, respectively; projecting an image to be recognized to the projection matrix to obtain the characteristic matrix of the image to be recognized, calculating a distance between the characteristic matrix of the image to be recognized and the characteristic matrix of each training image, and selecting the type of the characteristic matrix of the training image having the shortest distance away from the characteristic matrix of the image to be recognized as the type of the image to be recognized. Therefore, no matrix inversion operation is needed in the method, and the restraint of the inversion operation of a singular matrix is avoided; and the method is higher in robustness and wider in application range, compared with Two-Dimensional Linear Discrimination Analysis (2DLDA).

The invention provides a hyperspectral image target detection method based on tension linear discrimination analysis dimension reduction. The objective of the invention is to solve problems that in the current hyperspectral image target detection method, characteristics of spatial constraint enhancement under the condition of high scores are not fully considered, information excavation cannot be performed on the whole three-dimensional information and the detection precision is quite low. The method comprises steps of 1, acquiring three-order target tension blocks, three-order background tension blocks and three-order to-be-detected test sample tension blocks; 2, allowing sub-space after the projection of the target tension blocks, the background tension blocks and the to-be-detected test sample tension blocks to have the biggest separability; 3, projecting the target tension blocks, the background tension blocks and the to-be-detected test sample tension blocks to a tension sub-space with the biggest separability; 4, calculating the total distance from each to-be-detected test sample to the background and the target; and 5, setting a threshold value and if the gray scale value is larger than the threshold value, determining the pixel of the central point as the target, or else, determining pixel of the central point as the background. According to the invention, the method is applicable to image processing field.

The invention discloses a method for identifying rotation of the neck. The method for identifying rotation of the neck includes the steps that a wireless electrode is used for collecting an epidermis electromyographic signal of the neck of the human body and transmitting the epidermis electromyographic signal to an electric signal receiving device; after the electromyographic signal is pretreated, an analog signal is converted into an electric signal through an analog-to-electric conversion device; the electric signal is transmitted into a chip to calculate the characteristics of electromyographic data, the linear discrimination analysis LAD method is used for processing the electromyographic data; an LDA classifier is trained, accurate classification of actions is achieved by the LDA classifier; channel change of a television is achieved, and operation of adjusting the volume is achieved. With the method for identifying rotation of the neck, interaction between the neck and the television is achieved through rotation of the neck.

The standard model creating apparatus which provides a high-precision standard model used for: pattern recognition such as speech recognition, character recognition, or image recognition using a probability model based on a hidden Markov model, Bayesian theory, or linear discrimination analysis; intention interpretation using a probability model such as a Bayesian net; data-mining performed using a probability model; and so forth, the apparatus comprising: a reference model preparing unit (102) operable to prepare at least one reference model; a reference model storing unit (103) operable to store the reference model (121) prepared by the reference model preparing unit (102); and a standard model creating unit (104) operable to create a standard model (122) by calculating statistics of the standard model so as to maximize or locally maximize the probability or likelihood with respect to the at least one reference model stored in the reference model storing unit (103).

The invention belongs to the field of food analysis and detection and particularly relates to a steamed Lipu taro quality discrimination method based on sensory evaluation and electric nose analysis.The method comprises the steps that (1) the flavor characteristics of steamed Lipu taros are studied, and a sensory evaluation method is established; (2) electric nose detection on the steamed Lipu taros is performed; and (3) quality discrimination on the to-be-detected steamed Lipu taros is performed, wherein data collected by an electric nose sensor array during the electric nose detection in the step (2) is obtained, a corresponding relation between the data and sensory scores obtained in the step (1) is constructed, and according to the characteristic that electric nose sensors can quicklyrespond to different gases, a principal component analysis method, a linear discrimination analysis method and a load analysis method are adopted to analyze sensor characteristic values so as to quickly discriminate the quality grade of the to-be-detected steamed Lipu taros. Through the method, the quality of the Lipu taros treated at different steaming time can be discriminated and distinguishedrapidly and accurately; and the method has the advantages of being objective in result, good in repeatability, high in sensitivity, high in analysis speed, easy to operate, economical, efficient andthe like.

The invention discloses a voiceprint rapid verification method based on application scenes and application thereof. The method comprises the steps: determining a voiceprint judgment threshold based on an application scene after obtaining the application scene; acquiring a to-be-verified voice, inputting the to-be-verified voice into a probability linear discrimination analysis classifier, and outputting a voiceprint score by the probability linear discrimination analysis classifier; and judging whether the voiceprint score is greater than or equal to a voiceprint judgment threshold, if so, passing voiceprint verification, and otherwise, not passing voiceprint verification. The training process of the probability linear discrimination analysis classifier is a process of continuously adjusting parameters by taking the voice with known identity as input and taking the voiceprint score of the voice as theoretical output. According to the method, different threshold values are set for different application scenes, the problem that the data processing amount is too large is solved, verification data are improved, user experience is improved, and the method has great application prospects.