50 results about "Forest classification" patented technology

Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described. Embodiments apply a trained random forest classifier to low-resolution patches generated from the low-resolution input image to classify the low-resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier “votes” for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

The invention discloses a ground target classification method based on random forest and data rejection. The realization steps are: (1) preprocessing the training sample set; (2) extracting the training feature matrix; (3) training the random forest classification (4) preprocess the test sample; (5) extract the test feature vector; (6) calculate the ground target output probability vector; (7) judge whether the test sample is rejected; (8) if rejected, test The sample is used as an echo signal without target micro-motion characteristics; (9) If the judgment is not rejected, the category corresponding to the maximum value in the output probability vector is used as the ground target classification result of the test sample. The invention rejects clutter, deceptive interference and target signals without micro-motion, improves the classification and recognition rate of ground moving targets, and adopts a classifier with parallel processing capability to improve the real-time performance of the method.

The invention discloses a no-reference image quality evaluation method based on deep forest classification, comprising: step 1, image classification; step 2, extracting the color quality feature of the image; step 3, extracting the texture quality feature of the image; step 4, using In the deep forest classification model, the decision tree extracts different features, simulates the difference in perception of image quality by different people, and constructs a deep forest classification model to classify image quality, including multi-granularity scanning forest and cascading forest; step 5, based on image quality Features and their category labels, train the deep forest classification model, and obtain the probability that the test image belongs to different categories, that is, the statistical information of the subjective evaluation results of the image quality by different people; step 6, set the quality anchor, and combine the images belonging to different categories The probability of taking into account the differences in the subjective evaluation process to obtain the final image quality score; the no-reference image quality evaluation method described in the present invention uses a deep forest to simulate the difference in image quality cognition of different people, thereby giving an image The quality evaluation results have important theoretical significance and practical value.

The present application relates to a network traffic classification method, system and electronic equipment based on deep learning. The method includes: step a: capturing network traffic sample data; step b: extracting the global feature data set of the network traffic sample data through a deep learning classification algorithm; step c: constructing a random forest classification model according to the global feature data set , output the network traffic classification results through the random forest classification model. This application uses the extracted global features to train the random forest classification model. The results show stable classification performance, can handle very high-dimensional traffic data, and do not need to do feature selection. Compared with the prior art, the present application can effectively guarantee the high precision and high performance of network traffic classification, and at the same time, can improve classification efficiency, shorten training time, and reduce computing overhead.