Optimization of SIFT-Based Image Retrieval

In order to deal with the great discrepancy between the expectations of users and the real performance in image retrieval, some improvement on building tree, retrieval and matching methods were made with great success both in accuracy and in efficiency. More precisely, a new clustering strategy was firstly redefined during the building of vocabulary tree, which combined the classification and the conventional K-means method. Then a new matching method to eliminate the error caused by large-scale SIFT was introduced. What was more, a new unit mechanism was adopted to shorten the cost of indexing time. Finally, the numerical results show that an excellent performance is obtained after these improvements. A vocabulary tree with more distinguished nodes is achieved, of which the height is defined automatically and the index accuracy is enhanced greatly. Furthermore, a faster indexing procedure is realized, of which the indexing time is much less than 1 s.