Deep learning image target mapping and localization method based on weak supervision information

Abstract

The invention provides a deep learning image target mapping and positioning method based on weak supervision information. The method includes: using image data with category labels to train two deep convolutional neural network frameworks respectively to obtain a classification model M1 and a classification model M2, and obtain global parameterized learnable pooling layer parameters; use a new classification model M2 Perform feature extraction on the test image to obtain a feature map, and obtain a preliminary positioning frame through feature category mapping and threshold method according to the feature map; use the selective search method to extract candidate regions from the test image, and use the classification model M1 to screen out the candidate frame set; Perform non-maximum value suppression processing on the preliminary positioning frame and the candidate frame to obtain the final target positioning frame of the test image. The present invention introduces a global learnable pooling layer with parameters, which can learn better feature expressions about the target category j, and effectively obtain the position information of the target object in the image by using selective feature category mapping.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a deep learning image target mapping and positioning method based on weakly supervised information. Background technique [0002] With the development of deep learning technology represented by deep convolutional neural network, great breakthroughs have been made in the fields of image classification and image object recognition, and many influential academic researches and related industrial applications have been triggered. In the 2015 Global Large-Scale Visual Recognition Competition (ILSVRC), the deep residual model proposed by Microsoft Research Asia won the championship with a recognition error rate of 3.57%, and surpassed human recognition accuracy for the first time. [0003] The Regional Convolutional Neural Network (RCNN) proposed in 2014 was the first to use a deep convolutional network for image target detection tasks, and its performance was significantly im...

AI Technical Summary

Problems solved by technology

[0006] The disadvantage of the above-mentioned image mapping method in the prior art is: this method of pooling the feature map using the method of calculating the global average value or the global maximum value will cause the feature points after pooling to have insufficient representation ability for the original features.

Images