Research On Methods For Fast Image Retrieval

Large scale image retrieval is a research hotspot in computer vision,and it also has great application value.With the rapid development of multimedia and Internet,the database often contains hundreds of millions of pictures.Large scale image retrieval faces a series of problems including retrieval speed,sorting results,storage cost and so on.In these problems,how to improve the retrieval speed has been the key problem of large scale image retrieval.The dissertation focuses on the problem of improving the retrieval speed in large scale image retrieval.The main research work and achievements can be listed as fol-lows:First,the dissertation proposes a new multiple inverted indexes method based on multiple metrics.In the mainstream multiple inverted indexes method,multiple in-verted indexes are produced independently or by random partition,which fails to ade-quately describe the similarity between the images.In this dissertation,the problem of the complementarity between multiple inverted indexes has been proposed and multiple metrics(weighted Euclidean distance)has been introduced to improve the complemen-tarity between multiple inverted indexes.Two methods of generating multiple metrics have been proposed:simple random sampling(SRS)weighted method and Latin square sampling(LHS)weighted method.The LHS weighted method can produce more differ-ent weighted Euclidean distance by theoretical analysis.Experiments on several public datasets show the effectiveness of the proposed method.Second,the dissertation proposed a SAR image retrieval method based on fly algo-rithm.Unlike vector quantization with inverted indexing method,hashing is a method based on binary quantization.The fly algorithm can generate hash codes for images effectively by mimicking the fruit fly olfactory circuit,which is the best local sensi-tive hashing method.The quantification method of the original fly algorithm has been changed to speed up the image retrieval.Experiments on several public SAR datasets show the effectiveness of the proposed method.Third,the dissertation proposed a new Deep Supervised Multilevel Hashing(DSMH)method.The mainstream deep hashing methods cannot deal with complex relations among multi-label images well.A multilevel loss function is elaborately designed to distinguish partial similarity between multi-label images.The state-of-the-art the method,DMSSPH,and its variant DSTLH are proved to be two special cases of the proposed method(DSMH).Experiments on several public datasets show the effective-ness of the proposed method.