A neural network feature learning method based on image self-encoding

Abstract

The invention discloses a neural network feature learning method based on image self-encoding, and belongs to the technical fields of feature learning and image retrieval. Firstly, the segmented training image set corresponding to the training image set is constructed through the segmented labels of the multi-label image dataset, and then the weights of the convolutional neural network and the self-encoder neural network are initialized, and the self-encoder neural network is trained using the stochastic gradient descent method, and each training image is extracted. The samples correspond to the latent variables of the segmented image and are normalized. Then, use this hidden variable as the training target corresponding to the original image in the training set, train the convolutional neural network, and extract the feature vector corresponding to each image in the test set image library, by calculating the query image and each image in the image library The Euclidean distance between the eigenvectors, and the distances are arranged in ascending order to obtain similar image retrieval results. The invention enables the features extracted by the trained neural network to achieve more excellent retrieval effect on the multi-label retrieval task.

Description

technical field [0001] The invention relates to the fields of deep learning and image retrieval, in particular to a feature expression method in 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. How...

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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