Instance-level image search method based on multiple layers of feature representations

Abstract

The invention relates to the technical field of computer vision, in particular to an instance-level image search method based on multiple layers of feature representations. According to the method, feature representations, on different layers, of images are learnt through a deep convolution neural network model for instance-level image search so that different images of the same article can be effectively found. A coding learning process is introduced on the basis of a traditional network model, and features from multiple convolution layers are automatically encoded, so that the extracted features are more robust, and influences of background and noise data on the features are reduced. Meanwhile, the features integrate local information and class information. The invention further discloses a loss function based on multiple tasks. By optimizing the function, the learnt features have good generalization performance, and the learnt features can be well used for distinguishing inter-class images from intra-class images of different things.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an instance-level image search method based on multi-layer feature representation. Background technique [0002] In the last ten years, instance-level image search has attracted a lot of attention. The problem can be simply described as giving an image of an object, allowing you to find different images of the same object or images close to the object from the data set. The rise of this problem is mainly due to a demand of consumers in online shopping. Consumers hope to upload an image of an object, and then the sales information of the same image can be displayed on the website. For example, Ali’s Pailitao is based on this demand. Developed, but in fact so far, the user experience is still not good, mainly due to the following reasons: [0003] Most of the images on the website are images with background or watermark, and the interference factors are large. At the same...

AI Technical Summary

Problems solved by technology

In the research, it was found that the triplet loss function has a good effect on the distinction between objects, which is not provided by the softmax loss function, but the generalization ability of the triplet loss function for features is not as good as that of softmax, so the two lack must

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