Image Processing And Retrieval For Large Scale Database

With the rapid development of the Internet, efficient processing oflarge scale image databases has become an important issue. Internet space,micro-blogging, social network sites, and other variety of network servicesneed technologies for massive image storage and processing. This thesisfocuses on the effective processing of massive images, involving imageretargeting, image compression, and image retrieval.Today, network terminal equipments are quite different, personalcomputers, mobile phones, Tablet PC, and so on. There are hugedifferences among the quality, sizes and resolution of different displayscreens. This thesis seeks to develop a method based on the evaluation ofthe energy value of the image pixels, trying removing unimportant part ofthe picture while the important objects are preserved. For this reason, wedevelop the advanced seamlet carving image resizing method. It is acontent aware resizing method. It finds some seamlet paths with lowenergy values. Then achieve image resizing by removing (or expanding)these path.In processing massive picture data, storage and transmissiontechnologies become bottlenecks. The maintenance costs may be greatlyreduced if we can develop an effective compression method. However,how to control the degree of compression while preserving the visualeffect of the image is a key issue. The key of compression technology inthis thesis is to estimate the user tolerance threshold with an axiomaticimage quality evaluation method. The closer between the degree ofcompression and the tolerance threshold of user, the better result it does onreducing maintenance cost while keeping image visual effect for users. Inthis thesis, we proposed a self-adapt visual lossless compress method. In this method, an objective image quality evaluation method is used todetermine user’s visual effect of images. And it finds the best compressionquality to minimize image file size without damage visual effect with a fastiterative process.Like massive text retrieval, massive image retrieval is also a difficultproblem. Traditional text search make effective queries using a reverseindex on the dictionary of documents. However, there is no dictionaryconcept in image databases. Although content based image retrievaltechniques increase complexity, they are more valuable than tag basedimage search methods. Thus we need an effective content based imageretrieval technology to search images among massive image databases. Inthis thesis, we try to first establish dictionary from image contents, andthen process effective image search upon the reverse index of thedictionary. To improve the quality of retrieval result, we developed a fastimage correspondence method based on global structure projection torefine the retrieval output of index method. This correspondence methodattempts to build a relationship between pictures and match each keypointin the image according to the location information. The experimentalresults show that our methods work well on processing large scale imagedatabases. And these methods have a lot application prospects.