Two-stage image retrieval method based on convolutional neural network

Abstract

The invention provides a two-stage image retrieval method based on a convolutional neural network. The method comprises the following steps: adding a feature extraction layer between a convolutional layer and a dense connection layer of a VGG16 network to construct a convolutional neural network model; training the convolutional neural network model by using the training set and the verification set, and adjusting parameters of the convolutional neural network model by using back propagation; inputting the test set into a trained convolutional neural network model, mapping the feature vectorsby using hash function mapping to obtain binary hash codes, and classifying the vectors output by the dense connection layer by using a softmax classification function to construct a secondary index library; and inputting a to-be-retrieved image into the trained convolutional neural network model, and carrying out first-stage retrieval and second-stage retrieval. According to the method, further search is carried out under the corresponding image category, accurate classification and rapid retrieval of the images are achieved through classification optimization retrieval, the retrieval speed of similar features is increased, and the query efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to a two-stage image retrieval method based on a convolutional neural network. Background technique [0002] With the rapid growth of available image resources in various fields, effective image search methods are becoming more and more important. Content-based image retrieval (CBIR) aims to retrieve similar images by analyzing image content features such as color, texture, and layout. Therefore image representation and similarity measurement are key to the task. Convolutional Neural Network (CNN) has a powerful feature extraction ability, which can directly process images and eliminate the influence of different basic features. Therefore, deep CNNs significantly improve the performance of various vision tasks. These achievements are all attributed to the ability of CNNs to deeply learn rich mid-level image representations. However, since the features extracted by convol...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the technical problems that the existing image retrieval method has high computational cost, low search matching efficiency, and is not suitable for searching in large databases, the present invention proposes a convolution-based Two-stage image retrieval method of neural network, modifying the original model of VGG16 network for image retrieval, which can perform image retrieval more quickly, simply and efficiently, and is suitable for large data sets

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