Image Retrieval Based On Sift Feature Point Detection

Digital images are widely used in the society nowadays, so image retrieval techniques are researched by more and more experts. Presently, a variety of image retrieval techniques are plagued with lots of problems, and most of the technologies are text-based retrieval methods. Traditional image retrieval methods, which may have poor ability of anti-interference, matching level or matching efficiency, can not fully meet the needs of users.Considering some ways of current image retrieval techniques, we introduce a effectively practical image retrieval technology and point out the advantage of this method. In the direction of this way, we can only choose a part of the picture as the retrieving target, but rather than the whole image. After the retrieving target was submitted, the system will give the same or similar image from the image databases according to the similarity of the image at last.This method is contents-based. In the process of extracting the features of pictures, we apply the way of the extraction of SIFT features, extract the high-dimensional features and then quantize these features into a vocabulary to build the index. This theory is through clustering the similar image area for the same cluster, which can effectively reduce a variety of image noise on the retrieval effect, thereby improving system robustness and accuracy of retrieval. The traditional K-means clustering method is very ineffective in the process of image features vector quantization. We study the weakness of K-means clustering method, and then identify a more flexible soft cluster method. This way in which can make cluster quickly and at the same time raise the efficiency and precision of image index, so the method is more suit for large numbers of image databases. In order to address the problem that the spatial constraint is ignored by the model in the text retrieval literature, we present a new idea that we can use the space constraint relationship of the image features to resort the indexing results. Furthermore, the original index result can be expanded to search the second time to h further improve the retrieval accuracy.The method, which has been proved to be a good image retrieval method by the experiment, has advantages of high accuracy and short response time.