Accurate retrieval method for target on the basis of deep metric learning

Abstract

The invention discloses an accurate retrieval method for a target on the basis of deep metric learning. The method comprises the following steps that: in the iterative training of a deep neural network structure, in a process that the extracted characteristics of multiple extracted pictures of the same class of target object are processed, enabling the same class of target objects to mutually approach, and enabling different classes of target objects to be mutually far away, wherein the characteristic distance of the target objects with different class labels is greater than a preset distance, a distance between intra-class individuals with the similar attribute mutually approaches, and a distance between the intra-class individuals with different attributes is greater than a preset distance to obtain a trained deep neural network model; and adopting a trained deep neural network model to independently extract respective characteristics from pictures to be inquired and a preset reference picture, obtaining Euclidean distances between the characteristics of the queried picture and the reference picture, and sorting the distances from small to big so as to obtain an accurate retrieval target. By use of the method of the embodiment, an accurate retrieval problem of a vertical territory is solved.

Description

technical field [0001] The invention relates to computer vision technology, in particular to an accurate target retrieval method based on deep metric learning. Background technique [0002] Accurate object retrieval has always been a crucial problem in the computer field, and it is also the basis of application analysis such as object tracking and behavior analysis. Precise retrieval (also known as fine-grained recognition) aims to finely distinguish different types of visually similar object categories. For example, fine-grained vehicle recognition can identify a specific car model in a picture, such as "Audi A6 2015". In recent years, with the rapid development of large-scale parallel computing capabilities of computers and the successful application of deep convolutional neural networks, people have invested more research in the vertical field of a large number of fine-grained image classification, such as identifying different species of animals, plants, vehicles, Clot...

AI Technical Summary

Problems solved by technology

[0007] However, local part-based methods rely on precise part localization and suffer from poor performance in the absence of large viewing angle variations

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