Research On Deep Hash Algorithm And Its Application In Water Conservancy Image Retrieval

In today’s era,as the most intuitive and convenient carrier for information storage and communication,pictures are widely spread on the Internet,and the number of picture data has exploded.Based on this,the demand for image retrieval tasks is an important topic at present.With the advancement of water conservancy informatization in the water conservancy industry,more and more surveillance cameras are used to record the operation of various water conservancy facilities,and a large amount of image data is also generated.These water conservancy image data need to be intelligently analyzed,so water conservancy image retrieval methods are urgently needed.The combination of hash-based image retrieval methods and convolutional neural networks has produced a series of deep hashing methods,which can meet the above various needs.Starting from the construction of the deep hashing method,this paper proposes two brand-new deep hashing network models,and constructs a water conservancy image data set based on real conditions.The experiments of these two models prove that the deep hashing method is effective in water conservancy image retrieval and has obtained good results.The specific work of this paper is as follows:(1)Precoded Deeep Supervised Hashing is proposed.This method is based on a single-input deep hash network model and uses an optimal target hash coding algorithm to convert image labels into target hash codes,with multi-label soft difference Value loss and L2 quantization loss,using VGG19 as the feature extraction part,building an end-to-end deep hashing network model.Tests from multiple aspects have verified the rationality of the method setting.Tests on the MNIST,CIFAR-10 and NUS-WIDE image data sets show that in some cases,the performance is better than the current advanced methods.(2)Asymmetric Deep Pairwise Supervised Hashing is proposed.This method is based on the basic idea of other pair-wise deep hashing methods,and uses pair-wise similarity constraints as supervised learning information.Through the asymmetric network structure,all the similar constraint information contained in the image data set is extracted.Moreover,VGG19 is also used as the feature extraction part,and a loss function compatible with this method is designed to learn the relationship between image features and hash coding from image pairs.The effectiveness of this method has been verified through multiple experiments.Compared with the current advanced methods on CIFAR-10 and NUS-WIDE image data sets,this method has obvious advantages.(3)Starting from the actual situation,based on the water conservancy monitoring images stored in the water conservancy video information cloud platform of Jiangxi Province,and adding some network water conservancy images to construct a water conservancy image data set.Using the two deep hashing methods in this paper to test on this data set,good results have been obtained,verifying the effectiveness of the deep hashing method for water conservancy image retrieval tasks.