| 英文摘要 | With the information age, especially the advent of Internet and multimedia technology, we will confront with a wide variety of images every day. Therefore, how to extract useful information from the turbulent images becomes a very concerned topic. Current image retrieval system can be divided into two categories: the first one is based on SIFT feature clustering method, and then using vector space model and fast inverted index retrieval method; the second category is based on low-dimensional features, for example color, texture, shape features, and then using histogram intersection to realize the fast matching algorithm. The first method is robust, and uses the local features to retrieval the query image, which effectively reduces the defects caused by the image rotation, translation and twist transformation. The second type is based on the image global features, this method is simple, efficient and feature dimension is relatively low. But global features will blend the foreground and background together; it is difficult to meet accurate requirements of uses. To overcome the former two defects we propose two different fusion methods between image’s SIFT and color features to increase the retrieval accurate, in this paper. The first one is that, firstly we extract image’s SIFT key points and relocate it to the two dimensional image, and then transform its RGB color space to the color invariant space, secondly we create the histogram of the invariant space, lastly we use the frequency value as the tf-idf in the vector space model. The second one is that, we create the color invariant histogram directly and use the frequency value as the tf-idf. Our experiment results demonstrate that the first fusion method is the better one. To decrease the complexity of the higher dimensional cluster, in this paper, we find that the 128 dimensional SIFT feature is not well-distributed from the analysis of the feature extract process and the experiment results. Therefore, this paper analyzed the existing fast k-nearest neighbor algorithm based on a suitable for high dimensional data, Adaptive LSH SIFT algorithm, and applied to image retrieval Bag of Features Model code of the clustering; that effectively improved the speed of model clustering. On the basis of the above study, we developed a content-based image retrieval experimental system, and present a target based on hand-drawn sketch ideas to be retrieved. |
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