34 results about "Multinomial logistic regression" patented technology

The invention provides a human body behavior identification method based on an inertial sensor. The method comprises the steps of acquiring human body behavior data of testees by means of the inertial sensor, conducting sliding window segmentation on the acquired human body behavior data, conducting feature extraction on a triaxial accelerated speed subsequence and a triaxial angular speed subsequence which are obtained after sliding window segmentation, conducting feature fusion on a feature vector to form a sample set of the human body behaviors of the testees, conducting feature selection on the sample set by means of the least correlated maximum redundant algorithm and the Bayes regularization sparse polynomial logistic regression algorithm, obtaining the classification feature vectors of all human body behaviors of all the testees, obtaining a classifier model of each human body behavior by means of a fuzzy least square support vector machine, and obtaining a human body behavior identification result after the human body behavior data are tested by means of the fuzzy least square support vector machine. By means of the human body behavior identification method, self-adaptation and identification efficiency can be improved.

A system and method are disclosed for learning a random multinomial logit (RML) classifier and applying the RML classifier for scene segmentation. The system includes an image textonization module, a feature selection module and a RML classifier. The image textonization module is configured to receive an image training set with the objects of the images being pre-labeled. The image textonization module is further configured to generate corresponding texton images from the image training set. The feature selection module is configured to randomly select one or more texture-layout features from the texton images. The RML classifier comprises multiple multinomial logistic regression models. The RML classifier is configured to learn each multinomial logistic regression model using the selected texture-layout features. The RML classifier is further configured to apply the learned regression models to an input image for scene segmentation.

The invention discloses an SAR image segmentation method based on a deconvolution network and sketch direction constraint. The invention mainly solves the problem that existing segmentation technologies are inaccurate in SAR image segmentation. The method comprises the implementation steps of 1, sketching an SAR image, thus acquiring a sketch; 2, dividing pixel sub-spaces of the SAR image according to an area chart of the SAR image; 3, training the deconvolution network; 4, clustering filter directions; 5, segmenting the pixel sub-space with a hybrid aggregation structure by using the SAR image segmentation method based on the deconvolution network and the sketch direction constraint; 6, performing independent target segmentation based on a sketch line gathering characteristic; 7, performing line target segmentation based on a visual semantic rule; 8, segmenting the homogeneous area pixel sub-space by using a polynomial-based logistic regression prior model; and 9, combining segmentation results, thus acquiring an SAR image segmentation result. According to the method provided by the invention, the good segmentation effect of the SAR image is acquired, and thus the method can be used for performing semantic segmentation on the SAR image.

The invention discloses a method for classifying hyperspectral images on the basis of a combination of unmixing and adaptive end member extraction. The method is used for solving the technical problem of large errors of an existing method for classifying hyperspectral images on the basis of spectral unmixing. The technical scheme includes that the method comprises steps of roughly classifying the images, and extracting end member sets of various categories by the aid of a confusion matrix; linearly spectrally unmixing training samples in the various categories by the aid of the acquired end member sets, and acquiring optimal classification results by the aid of a probability classifier with an abundance value optimized on the basis of multinomial logistic regression; updating the end member sets of the various categories according to the classification results; iterating the procedure, and continuously optimizing the classifier so that the classification accuracy is improved. The method has the advantage that as shown by test results, the average accuracy of tests on a simulated data set, the average accuracy of tests on data of a true hyperspectral data set AVIRIS Indian Pine and the average accuracy of tests on data of a true hyperspectral data set ROSIS Pavia University are 81.98%, 62.19% and 82.38% respectively.

The invention discloses an SAR image segmentation method based on feature learning and sketch line constraint, mainly used for solving the problem that SAR image segmentation in the prior art is inaccurate. The SAR image segmentation method comprises the following implementation steps of: (1), sketching an SAR image; (2), according to an area chart of the SAR image, dividing a pixel subspace of the SAR image; (3), performing feature learning by adopting a deconvolution model; (4), constructing a direction feature vector and a length feature vector, and performing filter structure clustering; (5), performing codebook projection based on direction constraint; (6), dividing a hybrid aggregation structured surface feature pixel subspace of the SAR image; (7), performing independent target segmentation based on the sketch line aggregation feature; (8), performing line target segmentation based on a visual semantic rule; (9), performing segmentation of a pixel subspace in a homogeneous area by adopting a polynomial-based logistic regression prior model; and (10), combining to obtain an SAR image segmentation result. By means of the SAR image segmentation method disclosed by the invention, the good segmentation effect of the SAR image is obtained; and the SAR image segmentation method can be used for semantic segmentation of the SAR image.

The invention discloses an SAR image segmentation method based on a ridge wave filter and a deconvolution structural model, mainly used for solving the problem that SAR image segmentation in the prior art is inaccurate. The SAR image segmentation method comprises the following implementation steps of: (1), sketching an SAR image so as to obtain a sketch image; (2), according to an area chart of the SAR image, dividing a pixel subspace of the SAR image; (3), constructing a ridge wave filter set; (4), constructing a deconvolution structural model; (5), segmenting a hybrid aggregation structured surface feature pixel subspace by adopting the SAR image segmentation method based on the ridge wave filter and the deconvolution structural model; (6), performing independent target segmentation based on the sketch line aggregation feature; (7), performing line target segmentation based on a visual semantic rule; (8), performing segmentation of a pixel subspace in a homogeneous area by adopting a polynomial-based logistic regression prior model; and (9), combining segmentation results so as to obtain an SAR image segmentation result. By means of the SAR image segmentation method disclosed by the invention, the good segmentation effect of the SAR image is obtained; and the SAR image segmentation method can be used for semantic segmentation of the SAR image.

The invention discloses a hyperspectral image semi-supervised classification method based on comprehensive confidence. The method comprises the following steps: reading a hyperspectral image; Calculating a graph weight matrix; 8, performing adjacent connection on the sparse graph weight matrix; Calculating a normalized graph weight matrix; Obtaining an initial training set and a candidate set; Setting collaborative training iteration times and starting a training process; Training a polynomial logic regression classifier; Obtaining a prediction label of the candidate set sample by using a polynomial logic regression classifier; Obtaining prediction tags of the candidate set samples by using a semi-supervised graph classification method; Selecting two candidate samples with consistent prediction tags and corresponding prediction tags to form a protocol set, and forming a comprehensive confidence set by corresponding confidence coefficients; Screening out a protocol set sample with a comprehensive confidence coefficient higher than 99% and a corresponding prediction label, and forming an amplification set and adding the amplification set into a training set; Removing an amplificationset sample in the candidate set; And judging whether the training reaches a set number of times, if not, continuing iteration, and if yes, stopping iteration, and classifying the hyperspectral imagesby using the semi-supervised graph.

The invention discloses a stochastic gradient Bayesian SAR image segmentation method based on a sketch structure, mainly used for solving the problem that SAR image segmentation in the prior art is inaccurate. The stochastic gradient Bayesian SAR image segmentation method comprises the following implementation steps of: (1), sketching an SAR image to obtain a sketch image of the SAR image; (2), according to an area chart of the SAR image, and dividing a pixel subspace of the SAR image; (3), performing hybrid aggregation structured surface feature pixel subspace segmentation through a method based on a stochastic gradient variational Bayesian network model; (4), performing independent target segmentation based on the sketch line aggregation feature; (5), performing line target segmentation based on a visual semantic rule; (6), performing segmentation of a pixel subspace in a homogeneous area by adopting a polynomial-based logistic regression prior model; and (7), combining segmentation results to obtain a segmentation result of the SAR image. By means of the stochastic gradient Bayesian SAR image segmentation method based on the sketch structure disclosed by the invention, the good segmentation effect of the SAR image is obtained; and the stochastic gradient Bayesian SAR image segmentation method can be used for semantic segmentation of the SAR image.

The invention discloses an SAR image segmentation method based on ridgelet filters and a convolution structure model. The SAR image segmentation method based on ridgelet filters and a convolution structure model mainly solves the problem that in the prior art, segmentation of SAR images is not accurate. The SAR image segmentation method based on ridgelet filters and a convolution structure model includes the following steps: 1) sketching an SAR image, and obtaining a sketch image; 2) according to an area image of the SAR image, dividing the pixel subspace of the SAR image; 3) constructing a ridgelet filter set; 4) constructing a convolution structure learning model; 5) utilizing the SAR image segmentation method based on the ridgelet filters and the convolution structure model to segment the pixel subspace of a hybrid aggregation structure natural object; 6) based on the gathering feature of sketch lines, performing segmentation of an independent object; 7) based on visual sense semantic rules, performing segmentation of line object; 8) based on polynomial logic regression prior model, segmenting the pixel subspace of a formal area; and 9) combining the segmentation results. The SAR image segmentation method based on ridgelet filters and a convolution structure model can acquire good segmentation effect of SAR images, and can be used for semantic segmentation of the SAR images.

The present invention discloses a sketch structure-based mean field variational Bayes synthetic aperture radar (SAR) image segmentation method. The method mainly solves the problem in the prior art that the SAR image is not segmented accurately. The method comprises a first step of sketching the SAR image, so as to obtain a sketch of the SAR image; a second step of dividing pixel sub-spaces of the SAR image according to an area chart of the SAR image; a third step of segmenting based on the pixel sub-spaces of a hybrid gathering structure of a mean field variational Bayes deduction network model; a fourth step of segmenting based on an independent target of a sketch line gathering feature; a fifth step of segmenting based on a line target of a visual semantic rule; a sixth step of segmenting based on a homogeneous area pixel sub-space of a polynomial logic regression prior model; and a seventh step of combining segmentation results, so as to obtain a segmentation result of the SAR image. Through adoption of the method, a good segmentation effect of the SAR image is obtained and can be used for semantic segmentation of the SAR image.

The invention discloses a road black spot linear cause analysis method based on binary Logistic regression, and the method comprises the following steps: 1, dividing a road to be researched into n road segment units according to the length; 2, analyzing the safety of each road segment unit, and determining road segments with the lower safety as black spots; 3, classifying the traffic accidents happening at each black spot into K classes according to the types of accidents, and converting the number of various types of accidents happening at the road segment units into a binary variable; 4, selecting Z types of linear indexes for describing the linear features of each black spot unit; 5, analyzing the impact on the occurrence of different types of accidents from various linear indexes of the black spots through a binary Logistic regression model. The method can be used for determining the impact on various types of accidents from the linear features of the road, helps a road traffic safety administrative department find a linear problem of a block spot, and is of the instructive significance to the design and construction of a road in future.

The invention discloses a visual question answering problem solving method based on a complex network analysis method, which includes semantic concept network construction, nonrandom depth walk, imageand text feature fusion and classifier, Semantic Concept Networks (SCNs) are designed to mine co-occurrence patterns of concepts to enhance semantic representation, non-random depth walk realizes themapping of complex networks to low-dimensional features, On the basis of constructing image semantic concept network, depth walk algorithm is used to learn the potential relationship of nodes in semantic concept network, and the nodes in complex network are mapped to a low-dimensional feature vector, and polynomial logistic regression is used to fuse image and text features to solve the visual question answering problem. The invention excavates the concept symbiosis mode and the hierarchical structure of the cluster concept, effectively integrates the visual and semantic features of the image, as well as the natural language features, and provides a feasible way for solving the visual question answering problem.

The invention discloses a method of detecting artifacts in ICU patient records based on multivariate logistic regression. The method includes: calculating ICU probability of a patient by a multivariate logistic regression expression, and comparing the probability to a set threshold to establishing a binary classifier used to judge whether the ICU patient is going to die or not. The method has the advantages that presetting preconditions having predictive variables obeying normal distribution is not required; the predictive variables can be continuous or disperse; the number of predictive variables in a model is reduced as far as possible, and complexity of the model is reduced while prediction accuracy is guaranteed. The invention further discloses a system to implement the method. The system has the advantages that the problem of non-specificity in existing risk prediction models can be solved effectively; compared to existing models, the model allows prediction to be more accurate and specific.

The invention discloses a data analysis method based on quantile logistic regression, and relates to a data analysis method based on quantile logistic regression. When a traditional logistic regression model is used as a classifier, the given information is very limited, and the prediction accuracy of the model is very poor. The method comprises the steps of 1, data cleaning and preprocessing; 2,quantile logistic regression modeling is carried out; 3, parameters of the quantile logistic regression model is solved, and the parameters are solved to minimize the sum of weighted error absolute values; and 4 an unconstrained quantile regression objective function and a constrained quantile regression objective function are calculated. Researchers can research different groups more carefully, and more information can be obtained through data. Meanwhile, the quantile regression model is not sensitive to abnormal values, the prediction effect of the model is not affected by a small number ofabnormal values, and the robustness of the model is greatly improved.

The invention discloses a remote sensing image classification method based on Gabor features and EMAP features. The method comprises the steps: extracting Gabor features from a remote sensing image, constructing a Gabor feature kernel, extracting EMAP features from the remote sensing image, and constructing an EMAP feature kernel; fusing the constructed Gabor feature kernel and EMAP feature kernel by using a feature composite kernel framework to obtain a feature composite kernel; and on the basis of the obtained feature composite kernel, classifying samples in the remote sensing image through a multi-term logistic regression model. According to the method, the Gabor features and the EMAP features are combined with spectral information and spatial information, so that the adverse effect of original spectral data is avoided, the problems of unbalanced categories and few samples are solved, the problem of information redundancy caused by relatively strong correlation of the spectral information is effectively solved, and the classification precision is improved.