62results about How to "Increase the distance between classes" patented technology

The embodiment of the invention provides a real-time labeling method for breast ultrasonic focus areas based on artificial intelligence. The method includes the steps that a breast ultrasonic image video is divided into picture sets according to the timestamp order at frames; the picture sets are sequentially detected according to a focus-area detection classification model, the BI-RADS type levelis determined, and meanwhile the focus areas are determined; contour lines of all the focus areas are labeled in pictures, wherein the types of the contour lines are related to the BI-RADS type level; all the frame pictures are anew synthesized to form a video according to the timestamp order. The embodiment of the invention also provides an establishing method for the focus-area detection classification model, and the establishing method is used for establishing the focus-area detection classification model in the real-time labeling method for the focus areas. The embodiment of the inventionalso provides a real-time labeling system for the breast ultrasonic focus areas based on artificial intelligence. According to the real-time labeling method and system for the breast ultrasonic focusareas based on artificial intelligence and the establishing method for the focus-area detection classification model in the embodiment, the focus recognition capability is enhanced, the misdiagnosisrate is reduced, and the embodiment assists doctors in giving a more-accurate suggestion.

The embodiment of the invention discloses a face image processing method and device and a server. The method comprises the following steps: obtaining a to-be-processed human face image; inputting thehuman face image into a pre-trained convolution neural network model, and obtaining the classification data outputted when the convolution neural network model responds to the input of the human faceimage, wherein the convolution neural network model takes a loss function as a constraint condition, so as to enable the feature cosine value of each class of the classification data to approach to one; obtaining the classification data, and carrying out the content understanding of the human face image according to the classification data. The cosine value between a feature vector and a weight value of the loss function is enabled to approach to one, so as to achieve the convergence of inner-class distance. The convergence of inner-class distance enables the inner-class distance of the classification data to be increased, and the increase of the inner-class distance enables the classification data to be different more apparently. The increase of the robustness of data also can improve theaccuracy of content understanding.

The invention discloses a multi-modal face recognition method performing through face depth prediction. The method comprises the steps of data extracting, wherein the face segmentation and scaling ofDepth modal data are conducted, cascade network model training, face recognition network training, model merging and testing phase performing. According to the method, not only is the face Depth imagepredicted by a network model close to a real face Depth image, but also the between-class distance of the predicted Depth image is enhanced by a cascaded classification network, so that the predictedface Depth image has richer identification information. According to the method, a face Depth modal is predicted through a face RGB modal to increase the diversification of face modal data, and multi-modal face recognition can be achieved under the premise that the existing hardware of an RGB camera is not changed. By combining the RGB modal and the predicted Depth modal, the accuracy of the multi-modal face recognition is higher than the accuracy of the face recognition only using RGB modal data.

The invention discloses a human face expression recognition method based on Curvelet transform and sparse learning. The method comprises the following steps: 1, inputting a human face expression image, carrying out the preprocessing of the human face expression image, and cutting and obtaining an eye region and a mouth region from the human face expression image after processing; 2, extracting the human face expression features through Curvelet transform, carrying out the Curvelet transform and feature extraction of the human face expression image after preprocessing, the eye region and the mouth region, carrying out the serial fusion of the three features, and obtaining fusion features; 3, carrying out the classification recognition based on the sparse learning, and respectively employing SRC for classification and recognition of the human face Curvelet features and fusion features; or respectively employing FDDL for classification and recognition of the human face Curvelet features and fusion features. The Curvelet transform employed in the method is a multi-scale geometric analysis tool, and can extract the multi-scale and multi-direction features. Meanwhile, the method employs a local region fusion method, and enables the fusion features to be better in imaging representing capability and feature discrimination capability.

The present invention discloses a method and device of vehicle reidentification based on multiple objective function deep learning. The method comprises: employing first-class vehicle training data generated based on a vehicle image data set and a preset Softmax objective function to train the preset network parameters in a deep learning network frame, obtaining an optimal depth learning network frame, employing second-class vehicle training data generated based on the vehicle image data and a preset Triplet Loss ternary objective function to train the optimal network parameters in the depth learning network frame, obtaining a target depth learning network frame, and performing reidentification of the vehicle image to be identified according to the target depth learning network frame. Through adoption of the target depth learning network frame, the image features with high robustness and high reliability can be obtained to facilitate vehicle reidentification and effectively satisfy the requirement of the vehicle reidentification in the monitoring video.

The invention discloses a face recognition neural network training method, system and device and a storage medium, and the method comprises the following steps: obtaining a face image as a training set and a test set, and combining a loss function of a face recognition neural network with an adaptive additional loss function; inputting the preprocessed training set into a face recognition neural network for training; inputting the test set into the trained face recognition neural network, and verifying the recognition accuracy of the trained face recognition neural network. According to the invention, when the face recognition neural network is trained, the loss function is combined with an adaptive additional loss function to obtain a final loss function; the intra-class distance when theface images are classified is shortened through the final loss function, the inter-class distance when the face images are classified is increased, meanwhile, balance of multi-sample classes and few-sample classes is considered, when sample distribution is unbalanced, the generalization performance of the face recognition neural network can be guaranteed, and the accuracy and reliability degree of face recognition are improved.

The invention discloses a face recognition method, system and device based on centralized coordination learning, and the method comprises the following steps: obtaining a to-be-recognized face image,carrying out the face detection of the face image, and obtaining a first face image; After alignment processing is carried out on the first face image, a second face image with a preset size is obtained; inputting the second face image into a preset face recognition model based on centralized coordination learning for feature extraction, and obtaining a face feature vector of the second face image; and calculating cosine similarity by combining the face feature vector and a preset face database, and obtaining a face recognition result according to the cosine similarity. According to the invention, a face recognition model based on centralized coordination learning is adopted to carry out feature extraction on the face image, each feature is pulled to an original point and is respectively put into all quadrants, the inter-class distance is larger, the classification efficiency and recognition accuracy of the face are improved, and the method can be widely applied to the technical fieldof face recognition.

The invention belongs to the technical field of emotion recognition, particularly relates to a speech emotion recognition method and system based on ternary loss, and aims at solving the technical problem of accurately recognizing confusable emotion categories. For this purpose, the speech emotion recognition method comprises the following steps: framing the speech data to be measured so as to obtain a speech sequence of the specific length; temporal coding is performed based on the preset emotional temporal coding network and according to the speech sequence so as to obtain the emotional feature vectors corresponding to the speech sequence; and predicting the emotion category corresponding to the emotion feature vectors based on the preset speech emotion classifier and according to multiple preset real emotion categories. According to the speech emotion recognition method, the confusable speech emotion categories can be greatly recognized, and the method can be executed and implemented by the speech emotion recognition system.

The invention relates to a face model training method based on Center Loss improvement, and the method comprises the steps: (1),cutting and screening face images in an original total data set, and dividing the face images into a training set, a verification set, and a test set; (2) preprocessing the face images in the training set; (3) building a network structure, and optimizing a target loss function; (4) inputting the data in the training set into a network structure for training; (5) storing the face model; and (6) testing the face model by using the test set. On the basis that the face model has a certain classification capability, the intra-class aggregation capability of the model is enhanced, the targets of effectively increasing the inter-class distance and reducing the intra-class distance are achieved, and the accuracy and robustness of face recognition are improved.

The invention discloses a multi-view three-dimensional model retrieval method and a multi-view three-dimensional model retrieval system based on pairing depth feature learning. The multi-view three-dimensional model retrieval method comprises the steps of: acquiring two-dimensional views of a to-be-retrieved three-dimensional model at different angles, and extracting an initial view descriptor ofeach two-dimensional view; aggregating the plurality of initial view descriptors to obtain a final view descriptor; extracting potential features and category features of the final view descriptor respectively; performing weighted combination on the potential features and the category features to form a shape descriptor; and performing similarity calculation on the obtained shape descriptor and ashape descriptor of the three-dimensional model in a database to realize retrieval of the multi-view three-dimensional model. According to the multi-view three-dimensional model retrieval method, a multi-view three-dimensional model retrieval framework GPDFL is provided, potential features and category features of the model are fused, and the feature recognition capability and the model retrievalperformance can be improved.

The invention relates to a network traffic classification system and method based on depth discrimination features. The network traffic classification system comprises a preprocessing module and a model learning module, wherein the preprocessing module uses network flows with different lengths generated by different applications as input, and each network flow is expressed as a flow matrix with afixed size so as to meet the input format requirement of a convolutional neural network (CNN); and the model learning module trains the deep convolutional neural network by taking the flow matrix obtained by the preprocessing module as input under the supervision of a target function formed by a metric learning regularization item and a cross entropy loss item, so that the neural network can learnthe input flow matrix to obtain more discriminative feature representation, and the classification result is more accurate.

The invention discloses an image similarity calculation method based on an improved Soft-Max loss function. An activating function of the improved Soft-Max layer in an image identification network isan improved Soft-Max activation function. In a reverse propagation process, the improved Soft-Max loss function is utilized for updating a network weight. Compared with a traditional Soft-Max loss function, the improved Soft-Max loss function has an advantage of increasing a decision edge which is obtained through learning of an image identification network. In a testing period, a trained image identification model is utilized for performing characteristic vector extraction on two testing images, and cosine similarity between the characteristic vector is calculated. Compared with an image similarity threshold, if the cosine similarity is higher than or equal with the image similarity threshold, a fact that the two images are in the same kind is determined; and if the cosine similarity is lower than the image similarity threshold, a fact that the two images are in different kinds is determined.

The invention discloses a regional feature extraction face recognition method. The method is specifically implemented according to the following steps: 1, obtaining a to-be-recognized face image; 2, preprocessing the acquired face image by using a multi-task convolutional neural network, and marking key points of the face; 3, according to different key point position information, segmenting the human face into an expression variable region and an expression invariable region; 4, inputting the images of the expression variable region and the expression invariable region into a Gabor & block LBPfeature extraction channel to obtain a feature histogram containing face feature information; and 5, processing the feature histogram containing the human face feature information in the step 4 by using a linear discrimination method, and matching the processed human face feature information with human face features in a database to obtain a human face recognition result. According to the regional feature extraction face recognition method, the problem that the face recognition rate is low due to the influence of a non-limited environment in the prior art is solved.

The invention relates to a user recognition method for an advertising machine based on human faces. The advertising machine plays full-screen advertisements all the time under the condition that thereis no human face; when a human face appears on the screen, AR interaction is performed through the screen to capture an image of the human face, and an eigenvalue of the face image is calculated; andthe eigenvalue of the face image is matched with eigenvalues in a face database so as to perform recognition on the user. The method for calculating the eigenvalue of the face image is further improved, thereby being capable of improving the recognition speed on the basis of ensuring the face recognition accuracy. Identity recognition is performed on the advertising machine by using the human face as an identity feature, thereby being capable of pushing personalized advertisement contents in a targeted manner, avoiding the impact imposed on the user experience by fingerprint recognition in the prior art, and even avoiding a defect that two different users having similar fingerprints may pass fingerprint verification at the same time.

The invention provides a face recognition method. The face recognition method comprises the following steps: S1, obtaining face training test resources from a public data set; S2, preprocessing the image; S3, inputting the processed face data set into a convolutional neural network of a novel network structure of an improved NVM module to obtain a trained lightweight convolutional neural network;S4, inputting the face images in the test set into the trained lightweight convolutional neural network model, and judging whether the lightweight model can be accurately verified to effectively classify the face data set or not.

The invention provides an ANN (artificial neural network) adjustment method and device. The ANN includes a plurality of layers and is trained for classification reasoning. For example, the ANN may bea neural network trained for fine-grained image recognition. The method comprises the steps that a training data set is divided into a current category and other categories according to the currentlytargeted category, data features belonging to the current category are called as positive category features, and data features belonging to the other categories are called as negative category features; and the ANN is adjusted with a first loss function that specifies that the positive class feature is closer to a current class feature center of the current class than the negative class feature. According to the method, an effective loss function is designed from a loss function, the intra-class distance can be shortened, the inter-class distance can be increased, the classification effect canbe improved, and the prediction precision of the artificial neural network can be improved on the whole.