Content-based Image Indexing Research

Image retrieval technology is a research hotspot in the field of image application and has been widely used in many fields such as e-commerce and intellectual property.However,due to the generally high dimensionality of the image features,if the image features are directly used for image retrieval,not only the memory consumption is huge but also the retrieval efficiency is low.Therefore,this paper focuses on the image indexing technology and studies the local sensitive hash and the inverted index based on the word bag model.Through the research of these two common image indexing techniques,the efficiency of image retrieval is improved.This article mainly completed the following aspects of the work.In this paper,we study the local sensitive hash algorithm,index the global features of the image,and speed up the image retrieval.The basic principle of local sensitive hashing is introduced,and the local sensitive hash algorithm under two distance measures is selected as a local sensitive hash algorithm based on Hamming distance as the index algorithm of this chapter;the traditional local sensitive hash algorithm is used as a search part.After optimization,the concept of feature number is proposed.By comparing the feature numbers of images,the entire image database is avoided from being searched,and the time efficiency of the algorithm is effectively improved.This paper studies the image local feature indexing technology.The word bag model is used to reduce the dimensionality of the local features of the image,and the initial point of the traditional K-means clustering algorithm is improved,so that the visual words formed by the clustering effect are better;the word frequency vector is first clustered and then retrieved.The method effectively improves the retrieval efficiency of the word-bag model;the linear retrieval efficiency for the word-bag model is low,and the inverted index is studied in this paper.The inverted index is applied to the word-bag model to establish the inverted image.The index speeds up the image retrieval speed.The accuracy of the inverted index is insufficient.Based on the inverted index,the TF-IDF algorithm is added to improve the accuracy of the inverted index.