Generation method for image convolution characteristics based on top layer weight

Abstract

The invention discloses a generation method for image convolution characteristics based on the top layer weight. The generation method comprises steps of downloading images from the Internet and forming an image training set; training a model of a convolutional neural network; using the trained model of the convolutional neural network to extract depth convolution characteristics of different layers of each image respectively; using the obtained depth convolution characteristics to calculate a convolution weight image of the top layer; exerting the effect of the convolution weight image of the top layer on convolution characteristics of from a shallow layer to a high layer to obtain new convolution characteristics; obtaining depth characteristics added with the convolution full value of each image; and by extracting characteristics of the top layer convolution weight of a query image data set and an evaluation image data set respectively, calculating the similarity distance, performing the final similarity coupling, and obtaining a final retrieving result. Compared with prior art, the generation method is suitable for goods in the middle area and goods in any positions. The new top layer weight characteristics are more effective and accurate than the previous gauss weight, and the robustness and the accuracy of the image characteristics can be ensured.

Description

technical field [0001] The invention relates to image retrieval and automatic expression of visual content, in particular to a method for automatically obtaining the area weight of picture content. Background technique [0002] In the field of computer vision and multimedia, especially with the rapid growth of e-commerce digital pictures, under the current trend, the task of image retrieval is a very important and challenging task both in academia and industry. task. In the field of computer vision, better representation of pictures is the main driving force of research in recent years. When people see a picture of a product, people's attention will generally fall on the area where the product is located. Through related technologies, the location characteristics corresponding to the picture can be strengthened, so as to achieve the effect of highlighting product information and weakening the surrounding noise. The image features obtained in this way will have a good perfo...

AI Technical Summary

Problems solved by technology

For example, if you take a photo of someone else’s clothes with your mobile phone, the main technical difficulty you will encounter when matching the same clothes in the real picture with noise in the e-commerce data set is: the clothes in the real picture may be distorted , there are a lot of noise points around, and the picture quality is poor; while the same picture in the e-commerce data set is generally a professionally produced commercial picture

If you use street photos as search images, it will be difficult to match e-commerce images of the same style due to the large visual differences.

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