Research And Implementation Of Combining Visual Saliency And Hypergraph For Image Retrieval Algorithm

Due to the explosive growth of image data,there is an increasing demand for effective image retrieval algorithms.The content-based image retrieval technology uses the feature description extracted from the image to perform the retrieval,which not only saves the cost of manual labeling,but also makes the retrieval result more objective.However,only use the similarity of features cannot model the complex multivariate relationship.So that there is a huge difference between low-level features and high-level semantics which makes the retrieval result not so satisfaction.When the background of the retrieved image is complicated,the part that the user is more concerned about is easily ignored,resulting in poor retrieval performance.Based on the above analysis,this paper proposes an image retrieval algorithm that combines visual saliency and hypergraph.The main contributions are as follows:An image retrieval algorithm combining visual saliency and hypergraph is proposed.Utilizing the powerful data modeling capabilities of the hypergraph structure,the complex multivariate relationships between the data are fitted to make the search results more accurate.The visual saliency algorithm is a simulation algorithm for the human visual perception system.Through the visual saliency calculation,the weight of the saliency regional features in the retrieval process is increased,and the retrieval results are more in line with the user's expectations.After constructing the global hypergraph and the saliency hypergraph separately,and transforming the problem into the hypergraph cut problem,the fusion of saliency and hypergraph is realized by adding the combined weights of the graph structure loss of the global hypergraph and saliency hypergraph.To take advantage of each feature,iteratively optimizes the similarity vector of the retrieved image and the dataset image and the weight vector of the hypergraphs.The experimental results show that the image retrieval algorithm combining visual saliency and hypergraph is higher in retrieval accuracy than the image retrieval algorithm based on feature similarity and the algorithm using only hypergraph structure.Since the iterative optimization processing is in the online retrieval stage,it will affect the response speed of the system.Therefore,a multi-feature fusion algorithm is introduced to transfer the multi-feature fusion process to the offline learning stage,which ensures the performance of the retrieval system.After the multi-feature fusion process is transferred to the offline stage,the algorithm maintains better retrieval performance,and the retrieval efficiency of the online retrieval stage has been greatly improved.Finally,we used the image retrieval algorithm of combining visual saliency and hypergraph proposed in this paper and realized an image retrieval system based on B/S architecture.