Research Of Image Retrieval Based On Scale Invariant Features Transform

Based on scale invariant feature transform the continuous development of image retrieval at present, both in intelligent multimedia, digital technology, the Internet plays an irreplaceable role, scale invariant feature transform image retrieval in our actual life, also is changing, and this change is the image most widely involved in various fields, and the results we get higher demand for image retrieval technology. Image retrieval method based on scale invariant feature transform and has great difference between general retrieval, it does not require for more tedious manual identification, the visual impact and application, to fully demonstrate the change in each layer on each, so can read information of the image retrieval process,quickly call the database information.In this paper based on scale invariant feature transform of image retrieval technology of the new direction and new research, greatly increased the research of new height, will make a detail from the following several aspects.(1) of the image retrieval technology based on scale invariant feature transform with all parts of the system, to all aspects of the various characteristics and calculation is derived in detail; In part, a practical application of the theory derivation SIFT algorithm, because the SIFT algorithm has strong search precision,article under the different way, using the characteristics of the SIFT algorithm to realize image retrieval information correct results.(2) in the field of the research of image retrieval, it proved how similar. For distance measurement method combined with statistical measurement method and the calculation method of similarity was completed so that more clearly; In addition to strengthen the matching efficiency using the BBF- KDtree algorithm, compared it with the basic model structure, effectively completed the random sampling, use ofKD tree algorithm and the BBF algorithm two kinds of initialization method,reached under the condition of the amount of data, can still in speed relative matching; Some information in the process of matching error matching with RANSAC algorithm, not only in the process removes false matching points, but also retain the feature matching analysis, the correct matching points for us, and illustrates the mutual combination of these algorithms can achieve the optimal effect.(3) further use of Bag- of- Feature and Support Vector Machine(SVM) model of Support Vector Machine algorithm, the combination of the Bag- of- the basic framework of Feature model in order to realize the multiple cell marking processing,and in which indicate the similar characteristics of each cell is used to distinguish between; By support vector machine(SVM) image recognition, classification,analysis and processing, all images one by one child word segmentation bag complete classification, remove excess part, the simulation experiments, the algorithm plays a good role in accurate content.In the above introduction, clearly proved that the image retrieval based on scale invariant feature transform, the demand for information retrieval and use of SIFT algorithm of image to realize the accurate judgment, combining all kinds of measure,using the BBF- KDtree algorithm to eliminate the image database. In this paper, we proposed method and algorithm for the simulation experiment for many times,illustrates the methods and algorithm adopted in this paper are effective and accurate.