Neural network feature learning method based on image self coding

Abstract

The invention discloses a neural network feature learning method based on image self coding, which belongs to the technical field of feature learning and image retrieval. Firstly, a segmentation training image set corresponding to a training image set is constructed through a segmentation label of a multi-label image data set, weights of a convolutional neural network and a self coding neural network are then initialized, a stochastic gradient descent method is used for training the self coding neural network, and an implicit variable of a segmentation image corresponding to each training sample is extracted and normalized; and then, the implicit variable serves as a training target corresponding to an original training set image, the convolutional neural network is trained, a feature vector corresponding to each image in a test set image library is extracted, and through calculating Euclidean distances between feature vectors of a query image and each image in the image library and arranging the distances in an sequence from small to large, a similar image retrieval result is obtained. Thus, features extracted from the trained neural network achieve perfect retrieval effects in a multi-label retrieval task.

Description

technical field [0001] The invention relates to the fields of deep learning and image retrieval, especially a feature expression method designed into image retrieval, which can obtain more accurate similar images on a multi-label data set. Background technique [0002] With the development of multimedia and network technology, images, as the most intuitive way of expressing people's living conditions, play an increasingly important role in people's lives. Most images contain rich semantic information, how to find the images that users need in real life is a difficult problem and challenge. Excellent feature expression can not only represent the category information of the image, but also capture the relevant semantic information of the image. A large amount of image information is collected and utilized. However, combining image processing with computer vision technology to extract effective semantic expressions in images is the top priority in the field of computer vision....

AI Technical Summary

Problems solved by technology

However, the general lack of supervisory information in deep neural networks has limited the expressive ability of neural networks.

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